Triple
T20158851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VAL 206 |
E491648
|
entity |
| Predicate | designedBy |
P184
|
FINISHED |
| Object | Matra |
—
|
NE NERFINISHED |
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: [VAL 206, designedBy, Matra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matra Context triple: [VAL 206, designedBy, Matra]
-
A.
Matra
chosen
Matra is a French engineering and aerospace company known for its work in transportation systems, defense, and automotive technologies.
-
B.
Matra Djet
The Matra Djet is a mid-1960s French sports car, notable as one of the first mid-engined production road cars and produced under the Matra brand after originating as the René Bonnet Djet.
-
C.
Fuso
Fuso is a commercial vehicle manufacturer best known for its trucks and buses, operating as part of Daimler’s global automotive group.
-
D.
Citura
Citura is the public transport operator responsible for managing Reims’ urban transit network, including its tramway system, in northeastern France.
-
E.
Tecka
Tecka is a small town in the Chubut Province of Argentine Patagonia, serving as a local hub along regional road networks.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da6266c6888190bc1a3ecf24814d34 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e667e27aa88190a326288b992ea274 |
completed | April 20, 2026, 5:52 p.m. |
Created at: April 11, 2026, 11:34 p.m.