Triple
T1151510
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CAC 40 |
E23686
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object |
Bouygues
Bouygues is a major French industrial group primarily active in construction, real estate development, media, and telecommunications.
|
E132001
|
NE FINISHED |
How this triple was built (4 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: Bouygues | Statement: [CAC 40, hasComponent, Bouygues]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bouygues Context triple: [CAC 40, hasComponent, Bouygues]
-
A.
RATP group
RATP Group is a major French public transport operator that manages much of the Paris metro, tram, and bus networks and provides transit services internationally.
-
B.
Société Générale
Société Générale is a major French multinational investment and retail bank and financial services company headquartered in Paris.
-
C.
Thales Group
Thales Group is a French multinational company specializing in aerospace, defense, security, and transportation technologies and systems.
-
D.
TF1 Group
TF1 Group is a major French media conglomerate best known for operating France’s leading television channel TF1 and various other broadcasting and digital media assets.
-
E.
BESIX
BESIX is a major Belgian construction and engineering company known for delivering large-scale, high-profile projects worldwide.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Bouygues Triple: [CAC 40, hasComponent, Bouygues]
Generated description
Bouygues is a major French industrial group primarily active in construction, real estate development, media, and telecommunications.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bouygues Target entity description: Bouygues is a major French industrial group primarily active in construction, real estate development, media, and telecommunications.
-
A.
RATP group
RATP Group is a major French public transport operator that manages much of the Paris metro, tram, and bus networks and provides transit services internationally.
-
B.
Société Générale
Société Générale is a major French multinational investment and retail bank and financial services company headquartered in Paris.
-
C.
Thales Group
Thales Group is a French multinational company specializing in aerospace, defense, security, and transportation technologies and systems.
-
D.
TF1 Group
TF1 Group is a major French media conglomerate best known for operating France’s leading television channel TF1 and various other broadcasting and digital media assets.
-
E.
BESIX
BESIX is a major Belgian construction and engineering company known for delivering large-scale, high-profile projects worldwide.
- F. None of above. chosen
Provenance (5 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_69a493f0d32c8190ac74bad3c87f2641 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bc744e7c81908f8612f2aad28600 |
completed | March 1, 2026, 10:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac5eb5d36c8190916a43a5f41df144 |
completed | March 7, 2026, 5:21 p.m. |
| NEDg | Description generation | batch_69ac5f4756b08190b3dbaf64a9351836 |
completed | March 7, 2026, 5:24 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac600f78148190bd3109276f7b9e3a |
completed | March 7, 2026, 5:27 p.m. |
Created at: March 1, 2026, 7:44 p.m.