Triple
T5933914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Capgemini |
E131998
|
entity |
| Predicate | subsidiary |
P258
|
FINISHED |
| Object | Sogeti |
E556328
|
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: Sogeti | Statement: [Capgemini, subsidiary, Sogeti]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sogeti Context triple: [Capgemini, subsidiary, Sogeti]
-
A.
Sogeti
chosen
Sogeti is a professional services and technology consulting company specializing in IT and engineering solutions, operating as a subsidiary of Capgemini.
-
B.
SIGA Technologies
SIGA Technologies is a pharmaceutical company specializing in the development of antiviral treatments, particularly for smallpox and other orthopoxvirus infections.
-
C.
Tractebel
Tractebel is an international engineering and consulting company specializing in energy, water, and infrastructure projects.
-
D.
Indra Sistemas
Indra Sistemas is a Spanish multinational technology and defense company specializing in information technology, simulation, and advanced electronic systems for civil and military applications.
-
E.
Sogitec Industries
Sogitec Industries is a French company specializing in simulation, training systems, and technical publications, particularly for the aerospace and defense sectors.
- 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_69c0085c55dc8190aa90e242c956e2fa |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0389f6fc881909527b928838ffcdd |
completed | March 22, 2026, 6:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0e3affd748190a37e3cc60e58d6a6 |
completed | March 23, 2026, 6:54 a.m. |
Created at: March 22, 2026, 4 p.m.