Triple
T3218824
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dassault Aviation |
E67459
|
entity |
| Predicate | collaboratesWith |
P37
|
FINISHED |
| Object | Thales Group |
E81227
|
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: Thales Group | Statement: [Dassault Aviation, collaboratesWith, Thales Group]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thales Group Context triple: [Dassault Aviation, collaboratesWith, Thales Group]
-
A.
Thales Group
chosen
Thales Group is a French multinational company specializing in aerospace, defense, security, and transportation technologies and systems.
-
B.
Tractebel
Tractebel is an international engineering and consulting company specializing in energy, water, and infrastructure projects.
-
C.
GDF Suez
GDF Suez was a major French multinational energy company, primarily active in electricity and natural gas, that later rebranded as Engie.
-
D.
Thomson SA
Thomson SA was a major French electronics and media conglomerate known for its consumer electronics, broadcasting, and defense-related technologies.
-
E.
PSA Group
PSA Group was a major French automotive manufacturer best known for producing Peugeot, Citroën, and DS vehicles before merging to form Stellantis.
- 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_69ad858b8adc8190ad989712c87a476b |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adab0c48b481909d1bd9dc41dfa8c2 |
completed | March 8, 2026, 4:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b26245dfb08190a051ec6d2dbc63c6 |
completed | March 12, 2026, 6:50 a.m. |
Created at: March 8, 2026, 3:08 p.m.