Triple
T1719914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DirectShow |
E37365
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object |
GraphEdit
GraphEdit is a Windows utility for building, visualizing, and testing DirectShow filter graphs through a graphical interface.
|
E193858
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: GraphEdit | Statement: [DirectShow, hasComponent, GraphEdit]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GraphEdit Context triple: [DirectShow, hasComponent, GraphEdit]
-
A.
LisaDraw
LisaDraw was a pioneering graphical drawing and diagramming application for the Apple Lisa computer, notable for its early use of a mouse-driven, WYSIWYG interface.
-
B.
SmartArt graphics
SmartArt graphics are a collection of pre-designed visual diagrams in Microsoft Office that help users easily illustrate information, processes, and relationships in a polished, professional format.
-
C.
TextEdit
TextEdit is a simple, built-in macOS application for creating and editing plain text and rich text documents.
-
D.
LisaGraph
LisaGraph was a graphing and charting application included with Apple's Lisa computer, used to create visual data representations in the early graphical user interface environment.
-
E.
Plotly
Plotly is an interactive, open-source graphing and data visualization library widely used in Python for creating rich, web-based charts and dashboards.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: GraphEdit Triple: [DirectShow, hasComponent, GraphEdit]
Generated description
GraphEdit is a Windows utility for building, visualizing, and testing DirectShow filter graphs through a graphical interface.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: GraphEdit Target entity description: GraphEdit is a Windows utility for building, visualizing, and testing DirectShow filter graphs through a graphical interface.
-
A.
LisaDraw
LisaDraw was a pioneering graphical drawing and diagramming application for the Apple Lisa computer, notable for its early use of a mouse-driven, WYSIWYG interface.
-
B.
SmartArt graphics
SmartArt graphics are a collection of pre-designed visual diagrams in Microsoft Office that help users easily illustrate information, processes, and relationships in a polished, professional format.
-
C.
TextEdit
TextEdit is a simple, built-in macOS application for creating and editing plain text and rich text documents.
-
D.
LisaGraph
LisaGraph was a graphing and charting application included with Apple's Lisa computer, used to create visual data representations in the early graphical user interface environment.
-
E.
Plotly
Plotly is an interactive, open-source graphing and data visualization library widely used in Python for creating rich, web-based charts and dashboards.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a8861912dc8190931af43b4b9158a7 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa633934a4819083f2929da03453a8 |
completed | March 6, 2026, 5:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad8ae98cd88190af4dc46679b3d93f |
completed | March 8, 2026, 2:42 p.m. |
| NEDg | Description generation | batch_69ad957bd63c819099a508ca5c4102cc |
completed | March 8, 2026, 3:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad97b18f9c8190a9c5ed80b5ed0195 |
completed | March 8, 2026, 3:37 p.m. |
Created at: March 4, 2026, 7:30 p.m.