Triple
T22787641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aiman Ezzat |
E564012
|
entity |
| Predicate | hasEmployer |
P7
|
FINISHED |
| Object | Capgemini |
—
|
NE NERFINISHED |
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: Capgemini | Statement: [Aiman Ezzat, hasEmployer, Capgemini]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Capgemini Context triple: [Aiman Ezzat, hasEmployer, Capgemini]
-
A.
Capgemini
chosen
Capgemini is a global consulting, technology services, and digital transformation company headquartered in France.
-
B.
Infosys
Infosys is a leading Indian multinational IT services and consulting company known for its global technology solutions and innovation initiatives.
-
C.
Cognizant
Cognizant is a multinational information technology services and consulting company known for providing digital, technology, consulting, and operations services to clients worldwide.
-
D.
HCL Technologies
HCL Technologies is a global Indian IT services and consulting company known for providing software development, infrastructure management, and digital transformation solutions to enterprises worldwide.
-
E.
Accenture
Accenture is a global professional services company specializing in consulting, technology, and outsourcing solutions for businesses and governments worldwide.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e2455500788190b4b33030461f3bbd |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17c32de6481909ef358d16de98496 |
completed | April 29, 2026, 3:34 a.m. |
Created at: April 17, 2026, 3:29 p.m.