Triple
T1644956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Querétaro |
E35559
|
entity |
| Predicate | bordersWith |
P224
|
FINISHED |
| Object | Hidalgo |
E31143
|
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: Hidalgo | Statement: [Querétaro, bordersWith, Hidalgo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hidalgo Context triple: [Querétaro, bordersWith, Hidalgo]
-
A.
Hidalgo
chosen
Hidalgo is a central Mexican state known for its mountainous terrain, rich mining history, and diverse indigenous cultural heritage.
-
B.
Navojoa
Navojoa is a city in the southern part of the state of Sonora, Mexico, known as an agricultural and commercial center in the Mayo River valley.
-
C.
San Felipe
San Felipe is a historic city in central Chile known for its agricultural surroundings and role as a commercial and administrative center in the Aconcagua Valley.
-
D.
Madero
Madero is a coastal city in the Mexican state of Tamaulipas, known for its oil industry and the popular Miramar Beach on the Gulf of Mexico.
-
E.
Navarro
Navarro is a Spanish surname borne by numerous notable individuals across fields such as film, sports, politics, and academia.
- 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_69a88604618c81908b41f6429c431eb6 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90a41e5a08190b97dd1c0b12c662a |
completed | March 5, 2026, 4:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad60a26350819087e7a87b52561143 |
completed | March 8, 2026, 11:42 a.m. |
Created at: March 4, 2026, 7:28 p.m.