Triple
T8890039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Femme Fatale |
E211638
|
entity |
| Predicate | track |
P17929
|
FINISHED |
| Object | Gasoline |
E307436
|
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: Gasoline | Statement: [Femme Fatale, track, Gasoline]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gasoline Context triple: [Femme Fatale, track, Gasoline]
-
A.
Gasoline
chosen
Gasoline is a 1958 poetry collection by Beat Generation writer Gregory Corso, known for its energetic, surreal, and rebellious verse.
-
B.
Gasolina
"Gasolina" is a landmark reggaeton track by Daddy Yankee that helped popularize the genre worldwide in the early 2000s.
-
C.
GasGas
GasGas is a Spanish motorcycle manufacturer best known for its off-road, enduro, and trial bikes, and more recently its presence in MotoGP.
-
D.
Kerosene
"Kerosene" is a 2005 country song and breakthrough hit by Miranda Lambert, known for its fiery lyrics about revenge and empowerment.
-
E.
Vasoline
"Vasoline" is a 1994 grunge/alternative rock song by Stone Temple Pilots, known for its heavy riff, distinctive vocal effects, and prominent rotation on rock radio and MTV.
- 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_69ca83907954819096d52a245b635841 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc619188508190aacda410f0b4c98d |
completed | April 1, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfabf01d048190bed52b5b001d7ffa |
completed | April 3, 2026, noon |
Created at: March 30, 2026, 6:53 p.m.