Triple
T4781123
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | C. D. Broad |
E106166
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Harlesden |
E367724
|
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: Harlesden | Statement: [C. D. Broad, placeOfBirth, Harlesden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harlesden Context triple: [C. D. Broad, placeOfBirth, Harlesden]
-
A.
Harlesden
chosen
Harlesden is a residential district in northwest London known for its diverse community and strong Caribbean and Brazilian cultural influences.
-
B.
Willesden
Willesden is a residential district in the London Borough of Brent, known for its diverse community and good transport links in northwest London.
-
C.
Wood Green
Wood Green is a busy urban district and major shopping and transport hub in the London Borough of Haringey in north London.
-
D.
Hammersmith
Hammersmith is a district in West London known as a major commercial and transport hub along the River Thames.
-
E.
Kentish Town
Kentish Town is a residential and commercial district in north London known for its vibrant high street, music venues, and proximity to central London.
- 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_69bd43f3074c8190937e7b0a457fe9f1 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd65aa577c81909ec1b94e47810169 |
completed | March 20, 2026, 3:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6e3ff314c8190b66f8b0a7ae3c039 |
completed | March 27, 2026, 8:09 p.m. |
Created at: March 20, 2026, 1:21 p.m.