Triple
T6920487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tangy Tart Hot and Sweet |
E160170
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Padma Lakshmi |
E29992
|
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: Padma Lakshmi | Statement: [Tangy Tart Hot and Sweet, author, Padma Lakshmi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Padma Lakshmi Context triple: [Tangy Tart Hot and Sweet, author, Padma Lakshmi]
-
A.
Padma Lakshmi
chosen
Padma Lakshmi is an Indian-American author, model, and television host best known for hosting the cooking competition show "Top Chef."
-
B.
Neera Tanden
Neera Tanden is an American political consultant and policy expert who has held senior roles in Democratic administrations and think tanks, focusing on domestic and economic policy.
-
C.
Salma Lakhani
Salma Lakhani is a Canadian businesswoman and philanthropist who became the first Muslim and first South Asian to serve as a lieutenant governor in Canada.
-
D.
Sandra Lee
Sandra Lee is an American television chef and author known for her "Semi-Homemade" cooking concept and numerous Food Network shows.
-
E.
Shari Headley
Shari Headley is an American actress best known for her role as Lisa McDowell in the classic comedy film "Coming to America."
- 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_69c6883ab1008190a07129ff06f625d9 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6d9fa452c8190b2bea2d47309c889 |
completed | March 27, 2026, 7:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c79c79536c819092411ee37298a424 |
completed | March 28, 2026, 9:16 a.m. |
Created at: March 27, 2026, 2:26 p.m.