Triple
T16896641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Cotton Smith |
E424321
|
entity |
| Predicate | middleName |
P143
|
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: [John Cotton Smith, middleName, Cotton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cotton Context triple: [John Cotton Smith, middleName, 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.
Matsusaka cotton
Matsusaka cotton is a traditional Japanese textile renowned for its high-quality, finely woven fabric historically produced in the Matsusaka region.
-
E.
Cotton Market
Cotton Market is a historic commercial area traditionally associated with the trade and sale of cotton and related goods.
- 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_69d889da3e8c8190a2b118f383f0beac |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3c8d880208190ad2b2c8616b54ea3 |
completed | April 18, 2026, 6:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00c7ad473081908b1c1d9524cf64a6 |
completed | May 10, 2026, 6 p.m. |
Created at: April 10, 2026, 5:29 a.m.