Triple
T17211541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Divisadero Street |
E417742
|
entity |
| Predicate | hasNeighborhoodAlong |
P16140
|
FINISHED |
| Object | NoPa |
E512679
|
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: NoPa | Statement: [Divisadero Street, hasNeighborhoodAlong, NoPa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: NoPa Context triple: [Divisadero Street, hasNeighborhoodAlong, NoPa]
-
A.
NoPa neighborhood
chosen
The NoPa neighborhood is a trendy residential area in San Francisco known for its vibrant dining, nightlife, and cultural scene centered around Divisadero Street.
-
B.
NoMA
NoMA is the Norwegian Medicines Agency, the national authority responsible for regulating, approving, and monitoring medicines and medical devices in Norway.
-
C.
NOPA
NOPA is a trendy San Francisco neighborhood known for its vibrant dining scene, historic architecture, and proximity to the Panhandle and Golden Gate Park.
-
D.
NoMad
NoMad is a trendy Manhattan neighborhood known for its historic architecture, upscale hotels, and vibrant dining and nightlife scene centered around Madison Square Park.
-
E.
NoMa
NoMa is a rapidly developing neighborhood in Washington, D.C., known for its mixed-use developments, modern office buildings, and proximity to downtown.
- 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_69d886d779488190b131369541c04e7d |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42dc5a51481908d5ea0f9a1e9aa8b |
completed | April 19, 2026, 1:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01674fadc48190aa4a627ec9f72341 |
completed | May 11, 2026, 5:21 a.m. |
Created at: April 10, 2026, 5:38 a.m.