Triple
T19544027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | June Brown |
E488990
|
entity |
| Predicate | notableRole |
P22
|
FINISHED |
| Object | Dot Cotton |
—
|
NE NERFINISHED |
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: Dot Cotton | Statement: [June Brown, notableRole, Dot Cotton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dot Cotton Context triple: [June Brown, notableRole, Dot Cotton]
-
A.
Dot Cotton
chosen
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.
-
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.
Seaborn Cotton
Seaborn Cotton was a 17th-century New England Puritan minister and the son of prominent theologian John Cotton.
-
E.
Loonse denier
The Loonse denier was a medieval silver coin used as the primary monetary unit in the County of Loon in what is now Belgium.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8db5b6c8190984b61f91981f575 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e638750b288190aecdb0e18a1add62 |
completed | April 20, 2026, 2:30 p.m. |
Created at: April 10, 2026, 1:41 p.m.