Triple
T6789957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adobe Photoshop |
E155906
|
entity |
| Predicate | fileFormatSupport |
P8463
|
FINISHED |
| Object |
GIF
GIF (Graphics Interchange Format) is a widely used bitmap image format best known for supporting simple animations and lossless compression with a limited color palette.
|
E618925
|
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: GIF | Statement: [Adobe Photoshop, fileFormatSupport, GIF]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GIF Context triple: [Adobe Photoshop, fileFormatSupport, GIF]
-
A.
PNG
PNG is the three-letter ISO 3166-1 alpha-3 country code representing Papua New Guinea.
-
B.
GIFAS
GIFAS (Groupement des Industries Françaises Aéronautiques et Spatiales) is the French aerospace industries association representing and promoting France’s aviation, space, and defense sectors.
-
C.
JPEG
JPEG is a widely used digital image format that compresses photographic content to reduce file size while maintaining acceptable visual quality.
-
D.
Tiff
Tiff is a common shortened form of the given name Tiffany, often used as a casual or affectionate nickname.
-
E.
TIF
TIF is the former New York Stock Exchange ticker symbol for Tiffany & Co., the luxury jewelry and specialty retailer.
- 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: GIF Triple: [Adobe Photoshop, fileFormatSupport, GIF]
Generated description
GIF (Graphics Interchange Format) is a widely used bitmap image format best known for supporting simple animations and lossless compression with a limited color palette.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: GIF Target entity description: GIF (Graphics Interchange Format) is a widely used bitmap image format best known for supporting simple animations and lossless compression with a limited color palette.
-
A.
PNG
PNG is the three-letter ISO 3166-1 alpha-3 country code representing Papua New Guinea.
-
B.
GIFAS
GIFAS (Groupement des Industries Françaises Aéronautiques et Spatiales) is the French aerospace industries association representing and promoting France’s aviation, space, and defense sectors.
-
C.
JPEG
JPEG is a widely used digital image format that compresses photographic content to reduce file size while maintaining acceptable visual quality.
-
D.
Tiff
Tiff is a common shortened form of the given name Tiffany, often used as a casual or affectionate nickname.
-
E.
TIF
TIF is the former New York Stock Exchange ticker symbol for Tiffany & Co., the luxury jewelry and specialty retailer.
- 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_69c6881770fc8190972b2906390380f5 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d2ab4ce88190b6311e4d5aac758c |
completed | March 27, 2026, 6:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c71a8c445c8190abea97a04d648f52 |
completed | March 28, 2026, 12:02 a.m. |
| NEDg | Description generation | batch_69c71b186238819096ef6162f9068543 |
completed | March 28, 2026, 12:04 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c71b98b8008190a972cc0a215295c0 |
completed | March 28, 2026, 12:06 a.m. |
Created at: March 27, 2026, 2:15 p.m.