Triple
T3907849
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Azusa |
E87247
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | Azusa, CA |
E87247
|
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: Azusa, CA | Statement: [Azusa, abbreviation, Azusa, CA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Azusa, CA Context triple: [Azusa, abbreviation, Azusa, CA]
-
A.
Azusa
chosen
Azusa is a suburban city in the San Gabriel Valley region of Los Angeles County, California, located at the foothills of the San Gabriel Mountains.
-
B.
Torrance
Torrance is a coastal city in southwestern Los Angeles County, California, known for its suburban neighborhoods, automotive and aerospace industries, and one of the region’s largest concentrations of Japanese-American residents.
-
C.
El Cajon
El Cajon is a suburban city in Southern California’s East County region, located just east of San Diego.
-
D.
Anaheim
Anaheim is a major city in Orange County, California, best known as the home of the Disneyland Resort and a significant hub for tourism and entertainment in the region.
-
E.
Irvine
Irvine is a master-planned city in Orange County, California, known for its affluent residential communities, strong public schools, and concentration of technology and education industries.
- 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_69aed9424514819086e9c58adde6652d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeed13bb14819096842c6c82342524 |
completed | March 9, 2026, 3:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b51caf41c881909c5156480b46e794 |
completed | March 14, 2026, 8:30 a.m. |
Created at: March 9, 2026, 3:22 p.m.