Triple
T10350141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tony Tan Keng Yam |
E243859
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Tony |
E265285
|
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: Tony | Statement: [Tony Tan Keng Yam, givenName, Tony]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tony Context triple: [Tony Tan Keng Yam, givenName, Tony]
-
A.
Tony
Tony is the NATO reporting name for the Japanese World War II Kawasaki Ki-61 fighter aircraft.
-
B.
Tony
chosen
Tony is a common masculine given name, often used as a diminutive of Anthony or Antonio.
-
C.
Tony
Tony is a fictional character from the play "Master Class," which dramatizes a teaching session led by opera legend Maria Callas.
-
D.
Tony
The Tony is a prestigious American theater award presented annually to recognize excellence in Broadway productions.
-
E.
Tony
Tony is one of the central protagonists in Margaret Atwood’s novel "The Robber Bride," known for her intellectual, introspective nature and complex relationships with the other main characters.
- 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_69d381b22b8c8190aaed476be5f872a9 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e94796d881909e0b908de63a1aa3 |
completed | April 7, 2026, 11:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d7951e65948190a25e559ba94be3c7 |
completed | April 9, 2026, 12:01 p.m. |
Created at: April 6, 2026, 11:57 a.m.