Triple
T6612009
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sir Jackie Stewart |
E149257
|
entity |
| Predicate | racedForTeam |
P60038
|
FINISHED |
| Object | Matra |
E487771
|
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: Matra | Statement: [Sir Jackie Stewart, racedForTeam, Matra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matra Context triple: [Sir Jackie Stewart, racedForTeam, Matra]
-
A.
Matra
chosen
Matra is a French engineering and aerospace company known for its work in transportation systems, defense, and automotive technologies.
-
B.
Fuso
Fuso is a commercial vehicle manufacturer best known for its trucks and buses, operating as part of Daimler’s global automotive group.
-
C.
Citura
Citura is the public transport operator responsible for managing Reims’ urban transit network, including its tramway system, in northeastern France.
-
D.
Sitra
Sitra is a small island in Bahrain known for its residential communities, industrial facilities, and role in the country’s oil and gas infrastructure.
-
E.
Teves
Teves is an alternative Ashkenazi transliteration of the Hebrew month name Tevet, used in the Jewish calendar.
- 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_69c687ebc680819094caf71faba2efe2 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6af3778a8819094e83afed7c6596f |
completed | March 27, 2026, 4:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cbd651c08190a19513645fc781c9 |
completed | March 27, 2026, 6:26 p.m. |
Created at: March 27, 2026, 1:57 p.m.