Triple
T15970895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caroline Shaw |
E387318
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Orange
"Orange" is a Pulitzer Prize-winning composition by contemporary American composer Caroline Shaw, known for its inventive blend of classical and modern musical elements.
|
E1186311
|
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: Orange | Statement: [Caroline Shaw, notableWork, Orange]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orange Context triple: [Caroline Shaw, notableWork, Orange]
-
A.
Orange
Orange is a common English surname of likely Norman or French origin, shared by various individuals including the British singer Jason Orange.
-
B.
Orange
Orange is a historic town in southeastern France best known for giving its name and origin to the Dutch royal House of Orange-Nassau.
-
C.
Orange
Orange is a small suburban village in Cuyahoga County, Ohio, known for its residential character and proximity to the Cleveland metropolitan area.
-
D.
Orange
Orange is a citrus-flavored sports drink variety known for its bright, tangy taste and association with energy and hydration.
-
E.
Orange
Orange was the original name of the town now known as Hillsborough in North Carolina, reflecting its early colonial-era identity.
- 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: Orange Triple: [Caroline Shaw, notableWork, Orange]
Generated description
"Orange" is a Pulitzer Prize-winning composition by contemporary American composer Caroline Shaw, known for its inventive blend of classical and modern musical elements.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Orange Target entity description: "Orange" is a Pulitzer Prize-winning composition by contemporary American composer Caroline Shaw, known for its inventive blend of classical and modern musical elements.
-
A.
Orange
Orange is a Japanese rock band known for performing the opening theme song of the animated series "Generator Rex."
-
B.
Orange
Orange is a citrus-flavored sports drink variety known for its bright, tangy taste and association with energy and hydration.
-
C.
Orange
Orange is a common English surname of likely Norman or French origin, shared by various individuals including the British singer Jason Orange.
-
D.
Orange
Orange is a regional city in the Central Tablelands of New South Wales, Australia, known for its cool-climate wines, agriculture, and growing tourism industry.
-
E.
Orange
Orange is one of the primary lines of the Washington Metro rapid transit system, serving multiple stations across the Washington, D.C. metropolitan area.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e157291214819088d65e984609e42c |
completed | April 16, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffbe88fa308190942d37cf67458396 |
completed | May 9, 2026, 11:08 p.m. |
| NEDg | Description generation | batch_69ffbf3f40288190a59646124e06a864 |
completed | May 9, 2026, 11:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffbfddd0348190baab794f613c71bf |
completed | May 9, 2026, 11:14 p.m. |
Created at: April 10, 2026, 4:54 a.m.