Triple
T8463021
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haas F1 Team |
E200089
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | Haas |
E200089
|
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: Haas | Statement: [Haas F1 Team, shortName, Haas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haas Context triple: [Haas F1 Team, shortName, Haas]
-
A.
Haas
Haas is a German-origin surname borne by numerous individuals worldwide, including several notable figures in fields such as sports, science, and the arts.
-
B.
Haas F1 Team
chosen
Haas F1 Team is an American-owned Formula One racing team that competes in the FIA Formula One World Championship.
-
C.
Dallara
Dallara is an Italian race car manufacturer renowned for designing and building chassis for top-level motorsport series worldwide, including IndyCar.
-
D.
Mercedes
Mercedes is a courageous and compassionate housekeeper who secretly aids the Spanish Maquis resistance in Guillermo del Toro’s dark fantasy film "Pan’s Labyrinth."
-
E.
Mercedes
Mercedes is a coastal municipality in the Philippine province of Camarines Norte known for its fishing industry and nearby island attractions.
- 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_69ca83198c4c8190a337bf717d1813f5 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe4a251f08190840a7fc31ff528b5 |
completed | March 31, 2026, 3:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce39d5f50081908e273d5286a0d397 |
completed | April 2, 2026, 9:41 a.m. |
Created at: March 30, 2026, 6:10 p.m.