Triple
T13015542
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | West Broadway |
E322540
|
entity |
| Predicate | runsThrough |
P416
|
FINISHED |
| Object | SoHo |
E54021
|
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 Broadway, runsThrough, SoHo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SoHo Context triple: [West Broadway, runsThrough, SoHo]
-
A.
SoHo
chosen
SoHo is a fashionable Lower Manhattan neighborhood known for its cast-iron architecture, art galleries, and upscale boutiques.
-
B.
SoHo
SoHo is a vibrant commercial and entertainment district in Hong Kong known for its trendy restaurants, bars, and nightlife.
-
C.
Leather District
The Leather District is a small historic neighborhood in Boston known for its 19th-century brick warehouse buildings and former leather industry.
-
D.
Flatiron District
The Flatiron District is a Manhattan neighborhood known for its iconic Flatiron Building, historic architecture, and role as a hub for tech companies and trendy dining.
-
E.
SoHo Square
SoHo Square is a mixed-use commercial and retail center serving as a key shopping and dining destination in Homewood, Alabama.
- 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_69d807657e8c8190bd9435ee2f823845 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97ecd04748190ade2530ee5db35fe |
completed | April 10, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75469a3f08190a7e417872147b455 |
completed | May 3, 2026, 1:58 p.m. |
Created at: April 9, 2026, 8:50 p.m.