Triple
T182345
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arizona |
E3903
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Mesa |
E19620
|
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: Mesa | Statement: [Arizona, hasMajorCity, Mesa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mesa Context triple: [Arizona, hasMajorCity, Mesa]
-
A.
Mesa, Arizona
chosen
Mesa, Arizona is a large city in the Phoenix metropolitan area known for its desert climate, suburban communities, and role as a major spring training hub for Major League Baseball.
-
B.
San Carlos
San Carlos is a city in San Mateo County, California, located on the San Francisco Peninsula between Belmont and Redwood City.
-
C.
Durango
Durango is a state in north-central Mexico known for its rugged mountainous terrain, significant mining history, and role as a setting for classic Western films.
-
D.
Phoenix
Phoenix is the capital and largest city of the U.S. state of Arizona, known for its desert climate, rapid growth, and role as a major economic and cultural center in the American Southwest.
-
E.
Tucson
Tucson is a major city in southern Arizona known for its desert landscape, rich Native American and Mexican cultural influences, and the University of Arizona.
- 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_69a25497e2f08190a040f8c6e1842643 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a2592516748190a85ae58eec191f14 |
completed | Feb. 28, 2026, 2:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a31c91b22c8190a1d04983ae4fd793 |
completed | Feb. 28, 2026, 4:49 p.m. |
Created at: Feb. 28, 2026, 2:40 a.m.