Triple
T22718052
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cablofil |
E561785
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Legrand Group |
—
|
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: Legrand Group | Statement: [Cablofil, associatedWith, Legrand Group]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Legrand Group Context triple: [Cablofil, associatedWith, Legrand Group]
-
A.
Legrand
chosen
Legrand is a French multinational company specializing in electrical and digital building infrastructure solutions, including switches, sockets, and cable management systems.
-
B.
Schneider Electric
Schneider Electric is a French multinational company specializing in energy management and industrial automation solutions for homes, buildings, data centers, infrastructure, and industry.
-
C.
Cegelec
Cegelec is an international engineering and technology services company specializing in electrical, automation, and information systems for infrastructure and industry.
-
D.
Thales Group
Thales Group is a French multinational company specializing in aerospace, defense, security, and transportation technologies and systems.
-
E.
Tractebel
Tractebel is an international engineering and consulting company specializing in energy, water, and infrastructure projects.
- 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_69e2454fc984819088213b58ee87a002 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1790ecbc48190926d16b20b674dbd |
completed | April 29, 2026, 3:20 a.m. |
Created at: April 17, 2026, 3:19 p.m.