Triple
T2258055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | War & Leisure |
E49772
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Detail |
E245935
|
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: Detail | Statement: [War & Leisure, producer, Detail]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Detail Context triple: [War & Leisure, producer, Detail]
-
A.
Detail
chosen
Detail is an American record producer and songwriter known for crafting hit R&B and hip-hop tracks for major artists such as Beyoncé and Lil Wayne.
-
B.
Dim
Dim is the large but gentle rhinoceros beetle who performs as a circus "strongman" in Pixar's animated film *A Bug's Life*.
-
C.
DED
DED is the commonly used abbreviation for the ASME Design Engineering Division, a professional group within ASME focused on advancing the field of mechanical design engineering.
-
D.
Spare
Spare is Prince Harry, Duke of Sussex’s candid memoir detailing his life within the British royal family and his personal struggles and experiences.
-
E.
Total
Total is an American R&B girl group best known for their 1990s hits and frequent collaborations with Bad Boy Records artists.
- 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_69ae71c69f088190a38254a8a3670124 |
completed | March 9, 2026, 7:07 a.m. |
Created at: March 4, 2026, 7:48 p.m.