Triple
T11633776
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | West End |
E276464
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Soho |
E22316
|
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: Soho | Statement: [West End, contains, Soho]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Soho Context triple: [West End, contains, 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.
Westend
Westend is a residential and commercial locality in Berlin known for its affluent neighborhoods, green spaces, and proximity to the Olympic Stadium.
-
D.
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.
-
E.
Kensington Market
Kensington Market is a vibrant, historically multicultural neighborhood in Toronto known for its eclectic shops, diverse food offerings, and bohemian street culture.
- 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_69d6aafa51148190ab84940694c00235 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a25c0b00819095898d2b2445ecfb |
completed | April 10, 2026, 7:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ee87b12044819098a858edb2b16689 |
completed | April 26, 2026, 9:46 p.m. |
Created at: April 8, 2026, 9:39 p.m.