Triple
T794243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fiat |
E16981
|
entity |
| Predicate | hasCompetitor |
P1375
|
FINISHED |
| Object | Renault |
E100742
|
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: Renault | Statement: [Fiat, hasCompetitor, Renault]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Renault Context triple: [Fiat, hasCompetitor, Renault]
-
A.
Renault
chosen
Renault is a major French automobile manufacturer known for producing a wide range of passenger cars, commercial vehicles, and electric vehicles sold worldwide.
-
B.
Peugeot
Peugeot is a historic French automobile manufacturer known for producing a wide range of passenger cars and commercial vehicles, now operating as a core brand within the multinational automotive group Stellantis.
-
C.
Citroën
Citroën is a historic French automobile manufacturer known for its innovative engineering and distinctive car designs.
-
D.
DS Automobiles
DS Automobiles is a French premium automotive brand known for its avant-garde design, advanced technology, and luxury-focused vehicles.
-
E.
Opel
Opel is a German automobile manufacturer known for producing a wide range of passenger cars and light commercial vehicles for the European market.
- 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_69a4936cb7448190914f5fe4b8d81607 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a79b976c819085cd381bbd597ca5 |
completed | March 1, 2026, 8:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac5e92d6b0819091fad60317eee455 |
completed | March 7, 2026, 5:21 p.m. |
Created at: March 1, 2026, 7:38 p.m.