Triple
T18414981
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London Boy |
E441861
|
entity |
| Predicate | mentionsPlace |
P831
|
FINISHED |
| Object | Soho |
—
|
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: Soho | Statement: [London Boy, mentionsPlace, Soho]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Soho Context triple: [London Boy, mentionsPlace, Soho]
-
A.
Soho
chosen
Soho is a vibrant central London district famed for its nightlife, entertainment venues, and diverse cultural scene.
-
B.
Soho
Soho is an inner-city district of Birmingham, England, historically known for its industrial heritage and diverse local community.
-
C.
Soho
Soho is a vibrant dining, nightlife, and entertainment district in Hong Kong known for its steep streets, trendy bars, and international restaurants.
-
D.
Westend
Westend is a residential and commercial locality in Berlin known for its affluent neighborhoods, green spaces, and proximity to the Olympic Stadium.
-
E.
Westend
Westend is a prominent and affluent district in Frankfurt am Main, Germany, known for its elegant residential areas and concentration of banks and corporate offices.
- 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_69d8b9eb8a508190a942fd75ebd8b1dc |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e51a278fdc8190aa81a2ade682d942 |
completed | April 19, 2026, 6:08 p.m. |
Created at: April 10, 2026, 10:47 a.m.