Triple
T9367437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cotton Warburton |
E225437
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Cotton |
E713991
|
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: Cotton | Statement: [Cotton Warburton, givenName, Cotton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cotton Context triple: [Cotton Warburton, givenName, Cotton]
-
A.
Cotton
Cotton is a soft, natural fiber harvested from the seed pods of cotton plants and widely used in textiles and clothing.
-
B.
Cotton
chosen
Cotton is a common English surname with historical associations to several notable British families and figures.
-
C.
Cotten
Cotten is a surname most notably associated with American actor Joseph Cotten, a prominent figure in classic Hollywood cinema.
-
D.
Cotton Market
Cotton Market is a historic commercial area traditionally associated with the trade and sale of cotton and related goods.
-
E.
Cotton Tufts
Cotton Tufts was an 18th-century American physician and patriot from Massachusetts who was active in public affairs during the Revolutionary era.
- 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_69ca842cbddc819099d71ecec48cf9e5 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd507f9ed8819092967b204faa4408 |
completed | April 1, 2026, 5:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0f4010ea88190b056f57bab78bef9 |
completed | April 4, 2026, 11:20 a.m. |
Created at: March 30, 2026, 7:43 p.m.