Triple
T11700060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liberty department store |
E278098
|
entity |
| Predicate | hasSignatureProduct |
P3585
|
FINISHED |
| Object | Tana Lawn 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: Tana Lawn cotton | Statement: [Liberty department store, hasSignatureProduct, Tana Lawn cotton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tana Lawn cotton Context triple: [Liberty department store, hasSignatureProduct, Tana Lawn cotton]
-
A.
Seaborn Cotton
Seaborn Cotton was a 17th-century New England Puritan minister and the son of prominent theologian John Cotton.
-
B.
Cotten
Cotten is a surname most notably associated with American actor Joseph Cotten, a prominent figure in classic Hollywood cinema.
-
C.
Dot Cotton
Dot Cotton is a long-running, iconic character from the British soap opera EastEnders, known for her devout Christian faith, chain-smoking habit, and moral yet often troubled presence in Albert Square.
-
D.
Cotton
chosen
Cotton is a soft, natural fiber harvested from the seed pods of cotton plants and widely used in textiles and clothing.
-
E.
Cotton
Cotton is a common English surname with historical associations to several notable British families and figures.
- 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_69d6aafe02d881909900d54ad7d4af84 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a49a025881909377c81d3debf465 |
completed | April 10, 2026, 7:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ef833084988190b5004c93f68dc628 |
completed | April 27, 2026, 3:39 p.m. |
Created at: April 8, 2026, 9:40 p.m.