Triple
T3925350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Areva |
E93261
|
entity |
| Predicate | foundedAs |
P364
|
FINISHED |
| Object | Areva SA |
E93261
|
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: Areva SA | Statement: [Areva, foundedAs, Areva SA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Areva SA Context triple: [Areva, foundedAs, Areva SA]
-
A.
Areva
chosen
Areva was a French multinational group specializing in nuclear power and renewable energy technologies, known for its involvement in the entire nuclear fuel cycle.
-
B.
Électricité de France
Électricité de France is France’s state-owned electric utility company and one of the world’s largest producers and distributors of electricity, particularly known for its extensive nuclear power fleet.
-
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.
Tractebel
Tractebel is an international engineering and consulting company specializing in energy, water, and infrastructure projects.
-
E.
Dalkia
Dalkia is a French energy services company specializing in energy efficiency, district heating and cooling, and sustainable energy solutions for buildings and industry.
- 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_69aed96bfa1081908f7b30f2c647dee6 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeed7f3cc881909464db1970ba39ae |
completed | March 9, 2026, 3:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b52873d5a881909c4c29f06b94a467 |
completed | March 14, 2026, 9:20 a.m. |
Created at: March 9, 2026, 3:23 p.m.