Triple
T3088739
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dorothy Cotton |
E64437
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Cotton |
E3906
|
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: [Dorothy Cotton, familyName, Cotton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cotton Context triple: [Dorothy Cotton, familyName, Cotton]
-
A.
Cotton
chosen
Cotton is a soft, natural fiber harvested from the seed pods of cotton plants and widely used in textiles and clothing.
-
B.
Cotten
Cotten is a surname most notably associated with American actor Joseph Cotten, a prominent figure in classic Hollywood cinema.
-
C.
Cotton Tufts
Cotton Tufts was an 18th-century American physician and patriot from Massachusetts who was active in public affairs during the Revolutionary era.
-
D.
Coton
Coton is a small village and civil parish in South Cambridgeshire, England, located just west of the city of Cambridge.
-
E.
Sugarcane
Sugarcane is a 2017 EP by Nigerian singer Tiwa Savage that blends Afrobeats, R&B, and pop influences and helped solidify her status as a leading figure in contemporary African music.
- 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_69ad857c97d88190b26f9b1c90839c77 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada20b99a4819090c3d3e08ed556ad |
completed | March 8, 2026, 4:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1f8a4c1f08190a80efd190e4ed07f |
completed | March 11, 2026, 11:20 p.m. |
Created at: March 8, 2026, 3:03 p.m.