Triple
T12938515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LDLC |
E309577
|
entity |
| Predicate | hasSubsidiary |
P254
|
FINISHED |
| Object | LDLC.com |
E309577
|
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: LDLC.com | Statement: [LDLC, hasSubsidiary, LDLC.com]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LDLC.com Context triple: [LDLC, hasSubsidiary, LDLC.com]
-
A.
LDLC
chosen
LDLC is a French online retailer specializing in computer hardware, electronics, and high-tech equipment.
-
B.
Farnell
Farnell is a small rural village in the Angus council area of eastern Scotland, known for its agricultural surroundings and historic parish church.
-
C.
Hardware City
Hardware City is the nickname of New Britain, Connecticut, historically known for its prominent hardware manufacturing industry.
-
D.
Dragon Mart
Dragon Mart is a massive Chinese-themed retail and trading complex in Dubai, known as one of the largest hubs for Chinese products outside mainland China.
-
E.
Citycon
Citycon is a Nordic real estate company specializing in owning, developing, and managing urban shopping centers and mixed-use properties.
- 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_69d7bdfa933c8190b5a27aa4a08a19b7 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97dc8c0848190946e109ec98e4479 |
completed | April 10, 2026, 10:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6af6ef89881908ec3138b9f4a8b1a |
completed | May 3, 2026, 2:14 a.m. |
Created at: April 9, 2026, 5:43 p.m.