Triple
T9118800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 3 (Mexico City Metro) |
E218789
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Guerrero |
E240232
|
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: Guerrero | Statement: [Line 3 (Mexico City Metro), hasStation, Guerrero]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guerrero Context triple: [Line 3 (Mexico City Metro), hasStation, Guerrero]
-
A.
Guerrero
Guerrero is a coastal state in southwestern Mexico known for its mountainous terrain, including part of the Sierra Madre del Sur, and popular tourist destinations such as Acapulco.
-
B.
Guerrero
chosen
Guerrero is a Mexico City neighborhood and metro station area known for its central location and connectivity within the capital’s transit system.
-
C.
Guerrero Negro
Guerrero Negro is a town in Baja California Sur, Mexico, best known for its large salt production facilities and as a prime destination for gray whale watching.
-
D.
Guerro
Guerro is a minor tributary stream associated with the Panaro River in northern Italy.
-
E.
Navarrete
Navarrete is a Spanish surname most notably borne by Javier Navarrete, an acclaimed film composer known for his work on movies such as "Pan's Labyrinth."
- 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_69ca83dddd548190983b96c664f7f367 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca8a7c6d48190a015efd17a017ca1 |
completed | April 1, 2026, 5:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d047baf5e48190aab0eb19908fabfc |
completed | April 3, 2026, 11:05 p.m. |
Created at: March 30, 2026, 7:17 p.m.