Triple
T11713482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princesse Tam-Tam |
E278429
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Coton |
E203272
|
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: Coton | Statement: [Princesse Tam-Tam, character, Coton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Coton Context triple: [Princesse Tam-Tam, character, Coton]
-
A.
Coton
chosen
Coton is a small village and civil parish in South Cambridgeshire, England, located just west of the city of Cambridge.
-
B.
Cotton Tufts
Cotton Tufts was an 18th-century American physician and patriot from Massachusetts who was active in public affairs during the Revolutionary era.
-
C.
Tigri
Tigri is a locality in South Delhi, India, known primarily as a residential area that also hosts institutions such as the BSF Signal Training School.
-
D.
Veluws
Veluws is a Dutch Low Saxon dialect spoken in the Veluwe region of the Netherlands, closely related to other eastern Dutch dialects such as Achterhooks.
-
E.
Cotten
Cotten is a surname most notably associated with American actor Joseph Cotten, a prominent figure in classic Hollywood cinema.
- 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_69d6aaff2ce88190b4a1e4b341ad5377 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4be10088190854699385d1f6a95 |
completed | April 10, 2026, 7:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ef838562d08190b9a764e88c50d423 |
completed | April 27, 2026, 3:40 p.m. |
Created at: April 8, 2026, 9:40 p.m.