Triple
T13635225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hayes |
E325830
|
entity |
| Predicate | hasNearbyTown |
P3883
|
FINISHED |
| Object | Southall |
E403076
|
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: Southall | Statement: [Hayes, hasNearbyTown, Southall]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Southall Context triple: [Hayes, hasNearbyTown, Southall]
-
A.
Southall
chosen
Southall is a suburban district in West London, England, known for its large South Asian community and vibrant multicultural atmosphere.
-
B.
Wood Green
Wood Green is a busy urban district and major shopping and transport hub in the London Borough of Haringey in north London.
-
C.
Perry Barr
Perry Barr is a suburban area of Birmingham, England, known for its residential neighborhoods, major road junctions, and sporting venues such as the Alexander Stadium.
-
D.
Harlesden
Harlesden is a residential district in northwest London known for its diverse community and strong Caribbean and Brazilian cultural influences.
-
E.
Hammersmith
Hammersmith is a district in West London known as a major commercial and transport hub along the River Thames.
- 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_69d8076beddc8190a53156f5bea77f5e |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc5a616dc81908b8c1213e1d4beed |
completed | April 12, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdf06a3e208190a24e6cd9cb97e99c |
completed | May 8, 2026, 2:17 p.m. |
Created at: April 9, 2026, 9:51 p.m.