Triple
T893702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Butte County |
E19295
|
entity |
| Predicate | largestCity |
P235
|
FINISHED |
| Object | Chico |
E27975
|
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: Chico | Statement: [Butte County, largestCity, Chico]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chico Context triple: [Butte County, largestCity, Chico]
-
A.
Chico
chosen
Chico is a mid-sized city in Northern California known for California State University, Chico, and its large urban park, Bidwell Park.
-
B.
Salinas
Salinas is a prominent agricultural city in Northern California, often called the "Salad Bowl of the World" and known as the birthplace of author John Steinbeck.
-
C.
Merced
Merced is a city in California’s San Joaquin Valley known as a gateway to Yosemite National Park and home to the University of California, Merced.
-
D.
Twain Harte
Twain Harte is a small mountain resort town in California’s Sierra Nevada known for its pine forests, outdoor recreation, and proximity to Yosemite National Park.
-
E.
San Luis
San Luis is a residential and commercial district located in the eastern part of Lima, Peru.
- 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_69a4939d37188190848be3d426ebc9ae |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ad212cd8819091eb1b7d606f5afd |
completed | March 1, 2026, 9:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7c02772208190ac86dd885728e89c |
completed | March 4, 2026, 5:16 a.m. |
Created at: March 1, 2026, 7:39 p.m.