Triple
T6006306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Legrand |
E133717
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object | Wiremold |
E133717
|
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: Wiremold | Statement: [Legrand, brand, Wiremold]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wiremold Context triple: [Legrand, brand, Wiremold]
-
A.
Hager
Hager is the surname of Jenna Bush Hager, an American television personality, author, and daughter of former U.S. President George W. Bush.
-
B.
Hirschmann
Hirschmann is a surname of German origin borne by various notable individuals across fields such as diplomacy, engineering, and academia.
-
C.
Legrand
chosen
Legrand is a French multinational company specializing in electrical and digital building infrastructure solutions, including switches, sockets, and cable management systems.
-
D.
Mitre Corporation
Mitre Corporation is a not-for-profit organization that operates federally funded research and development centers, providing systems engineering and advanced technology support primarily to U.S. government agencies.
-
E.
Eaton’s
Eaton’s was a major Canadian department store chain that became a retail icon and helped shape downtown shopping districts across the country.
- 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_69c00872444c8190bfaf1739dcec765c |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04f128354819088971ee398cbda77 |
completed | March 22, 2026, 8:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c10895559081908b9efdd32ecef37f |
completed | March 23, 2026, 9:32 a.m. |
Created at: March 22, 2026, 4:06 p.m.