Triple
T704124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Coca-Cola Company |
E14062
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object |
Sprite
Sprite is a popular lemon-lime flavored soft drink known for its crisp, caffeine-free taste and global presence.
|
E85132
|
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: Sprite | Statement: [The Coca-Cola Company, brand, Sprite]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sprite Context triple: [The Coca-Cola Company, brand, Sprite]
-
A.
Pixel
Pixel is Google's flagship line of Android smartphones known for their clean software experience and advanced camera capabilities.
-
B.
SPLASH
SPLASH is a major annual ACM conference focused on programming languages, software engineering, and related systems research.
-
C.
Loop
The Loop is Chicago’s central business district and downtown core, known for its dense cluster of skyscrapers, cultural institutions, and historic elevated train system.
-
D.
Loop
Loop is a Microsoft 365 collaborative workspace app that lets teams create, share, and co-edit dynamic content blocks in real time across Microsoft’s productivity tools.
-
E.
Terminal 2D
Terminal 2D is a passenger terminal at Paris Charles de Gaulle Airport, serving as one of the facilities handling flights and travelers at this major international hub.
- 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: Sprite Triple: [The Coca-Cola Company, brand, Sprite]
Generated description
Sprite is a popular lemon-lime flavored soft drink known for its crisp, caffeine-free taste and global presence.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sprite Target entity description: Sprite is a popular lemon-lime flavored soft drink known for its crisp, caffeine-free taste and global presence.
-
A.
Pixel
Pixel is Google's flagship line of Android smartphones known for their clean software experience and advanced camera capabilities.
-
B.
SPLASH
SPLASH is a major annual ACM conference focused on programming languages, software engineering, and related systems research.
-
C.
Loop
The Loop is Chicago’s central business district and downtown core, known for its dense cluster of skyscrapers, cultural institutions, and historic elevated train system.
-
D.
Loop
Loop is a Microsoft 365 collaborative workspace app that lets teams create, share, and co-edit dynamic content blocks in real time across Microsoft’s productivity tools.
-
E.
Terminal 2D
Terminal 2D is a passenger terminal at Paris Charles de Gaulle Airport, serving as one of the facilities handling flights and travelers at this major international hub.
- 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_69a493494ec48190ae6751683625a9ba |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a533fa788190bba0f55655469c46 |
completed | March 1, 2026, 8:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a5dcae2ef88190a9ea1604429f048a |
completed | March 2, 2026, 6:53 p.m. |
| NEDg | Description generation | batch_69a5df14e1788190bb2f2cc87cadcb40 |
completed | March 2, 2026, 7:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a5ff5dd4808190bb8ae25fbdca0075 |
completed | March 2, 2026, 9:21 p.m. |
Created at: March 1, 2026, 7:36 p.m.