Triple
T7134357
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bajío |
E166267
|
entity |
| Predicate | majorCity |
P316
|
FINISHED |
| Object | Celaya |
E329258
|
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: Celaya | Statement: [Bajío, majorCity, Celaya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Celaya Context triple: [Bajío, majorCity, Celaya]
-
A.
Celaya
chosen
Celaya is a major city and industrial municipality in the Mexican state of Guanajuato, known for its manufacturing sector and traditional cajeta (goat’s milk caramel).
-
B.
Monclova
Monclova is an industrial city in northern Mexico known as a major steel-producing center in the state of Coahuila.
-
C.
Irapuato
Irapuato is a Mexican professional football club based in the city of Irapuato, Guanajuato, known for its passionate fan base and history in the country’s lower divisions.
-
D.
Xalapa
Xalapa is a city in eastern Mexico known as the capital and cultural center of the state of Veracruz.
-
E.
Cadereyta Jiménez
Cadereyta Jiménez is a municipality in the Mexican state of Nuevo León, known for its oil refinery and agricultural activities within the Monterrey metropolitan area.
- 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_69c68884a9388190af42f90d1c1a7151 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e68f15bc8190a4d82b8ee388f497 |
completed | March 27, 2026, 8:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8de8efd648190b70b4299ecae32c7 |
completed | March 29, 2026, 8:10 a.m. |
Created at: March 27, 2026, 2:45 p.m.