Triple
T7711037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Low Desert |
E174750
|
entity |
| Predicate | majorCity |
P316
|
FINISHED |
| Object | Brawley |
E203554
|
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: Brawley | Statement: [Low Desert, majorCity, Brawley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brawley Context triple: [Low Desert, majorCity, Brawley]
-
A.
Brawley
chosen
Brawley is a small agricultural city in Southern California’s Imperial Valley, known for its farming industry and desert climate.
-
B.
Palm Desert
Palm Desert is a resort city in Southern California known for its golf courses, upscale shopping, and desert landscapes.
-
C.
Grover Beach
Grover Beach is a small coastal city in California known for its beach access, dunes, and relaxed seaside community.
-
D.
Atascadero
Atascadero is a small city in California’s Central Coast region known for its historic City Hall, wine country surroundings, and proximity to both coastal and inland attractions.
-
E.
Oceanside
Oceanside is a coastal city in northern San Diego County known for its beaches, historic wooden pier, and laid-back Southern California surf 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_69c6995b3e8c8190833108f883d5f53c |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702ad6b788190892b96ab523d111f |
completed | March 27, 2026, 10:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8b505c7f88190ac058c1a27d758ca |
completed | March 29, 2026, 5:13 a.m. |
Created at: March 27, 2026, 4:04 p.m.