Triple
T2258053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | War & Leisure |
E49772
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Happy Perez |
E245217
|
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: Happy Perez | Statement: [War & Leisure, producer, Happy Perez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Happy Perez Context triple: [War & Leisure, producer, Happy Perez]
-
A.
Happy Perez
chosen
Happy Perez is an American record producer and songwriter known for crafting smooth, R&B-infused hits for artists like Miguel, Mariah Carey, and H.E.R.
-
B.
Gomez
Gomez is a common Spanish-origin surname borne by numerous notable individuals across fields such as entertainment, sports, and politics.
-
C.
Pepa
Pepa is a traditional Assamese wind instrument, typically made from buffalo horn, used in folk and Bihu music.
-
D.
Totó
Totó is a neighborhood located in the city of Recife, in the state of Pernambuco, Brazil.
-
E.
Paco
Paco is a riverside district in Manila, Philippines, known for its historic sites, markets, and dense urban neighborhoods.
- 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_69a88aaa9250819095e127d0d77e8a32 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc15839fc8190b17e040c4c765a8c |
completed | March 7, 2026, 6:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae89477bb481908ce66fac62181f1f |
completed | March 9, 2026, 8:48 a.m. |
Created at: March 4, 2026, 7:48 p.m.