Triple
T15469891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coffee Town |
E372130
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Will |
E3364
|
NE FINISHED |
How this triple was built (2 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: Will | Statement: [Coffee Town, mainCharacter, Will]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Will Context triple: [Coffee Town, mainCharacter, Will]
-
A.
Will
chosen
Will is a common shortened form of the given name William, frequently used as a familiar or informal first name.
-
B.
Will
Will is a film or television production featuring actress Olivia DeJonge in its cast.
-
C.
Wills
Wills is a surname most notably associated with Childe Harold Wills, an early automotive engineer and key collaborator of Henry Ford in the development of the Model T.
-
D.
Wills
Wills is an Indian cigarette brand historically known for its prominent sponsorship of major cricket tournaments and events.
-
E.
Wil
Wil is a common shortened form of the given name Willem, often used as an informal or familiar nickname.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d85cc8bd308190886949510b42e764 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f6b49788190b270fdfe92646842 |
completed | April 16, 2026, 1:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff2d0543f881909dfbbc77f2a96a1a |
completed | May 9, 2026, 12:48 p.m. |
Created at: April 10, 2026, 3:33 a.m.