Triple
T12597657
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Queen for a Day |
E300772
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object | Tenneco |
E948050
|
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: Tenneco | Statement: [Queen for a Day, productionCompany, Tenneco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tenneco Context triple: [Queen for a Day, productionCompany, Tenneco]
-
A.
Tenneco
chosen
Tenneco is a global automotive parts manufacturer known for producing ride performance, clean air, and powertrain components for vehicle manufacturers and the aftermarket.
-
B.
Denso Corporation
Denso Corporation is a leading global automotive components manufacturer headquartered in Japan and a key supplier of advanced technologies and systems to major automakers worldwide.
-
C.
Mahle GmbH
Mahle GmbH is a German automotive parts manufacturer known worldwide for producing engine components, filtration systems, and thermal management solutions for vehicles.
-
D.
Eaton
Eaton is a small town located within Madison County in the state of New York, United States.
-
E.
Eaton
Eaton is a surname most notably associated with American decathlete and Olympic gold medalist Ashton Eaton.
- 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_69d7bdea2ca881908f379526c13b1145 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954cf33b88190bff339fcd3142cc8 |
completed | April 10, 2026, 7:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65ec75fc08190aa13cbb0161eb35c |
completed | May 2, 2026, 8:29 p.m. |
Created at: April 9, 2026, 5:08 p.m.