Triple
T6798662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santa María del Oro |
E156123
|
entity |
| Predicate | roadAccessFrom |
P22549
|
FINISHED |
| Object | Tepic |
E173358
|
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: Tepic | Statement: [Santa María del Oro, roadAccessFrom, Tepic]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tepic Context triple: [Santa María del Oro, roadAccessFrom, Tepic]
-
A.
Tepic
chosen
Tepic is the capital city of the Mexican state of Nayarit, known as a regional commercial and transportation hub in western Mexico.
-
B.
Tepatitlán de Morelos
Tepatitlán de Morelos is a prominent city in Mexico known for its agricultural production, religious traditions, and vibrant regional culture.
-
C.
Tulancingo
Tulancingo is a historic city in central Mexico known for its colonial architecture, regional commerce, and cultural traditions within the state of Hidalgo.
-
D.
Celaya
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).
-
E.
Juanacatlán
Juanacatlán is a Mexico City Metro station on Line 1, located near the Condesa and San Miguel Chapultepec neighborhoods.
- 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_69c6881844448190a65822d9b39d7f88 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d2cb6b2881909b30bb8020a9d3bf |
completed | March 27, 2026, 6:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c79428801c8190817a95d94ff180b9 |
completed | March 28, 2026, 8:41 a.m. |
Created at: March 27, 2026, 2:15 p.m.