Triple
T22515556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red de San Luis square |
E556636
|
entity |
| Predicate | nearMetroStation |
P33877
|
FINISHED |
| Object | Callao station |
—
|
NE NERFINISHED |
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: Callao station | Statement: [Red de San Luis square, nearMetroStation, Callao station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Callao station Context triple: [Red de San Luis square, nearMetroStation, Callao station]
-
A.
Callao station
chosen
Callao station is a subway stop on Buenos Aires’ underground network, serving passengers in the central area of the city.
-
B.
Callao metro station
Callao metro station is an underground station on the Madrid Metro network located in the central Gran Vía area of Spain’s capital.
-
C.
Lima station
Lima station is an underground metro station on Buenos Aires’ Line A, serving the city’s central area.
-
D.
Miguel Grau station
Miguel Grau station is a passenger stop on Line 1 of the Lima Metro rapid transit system in Lima, Peru.
-
E.
Miramar station
Miramar station is a passenger rail station on the Valparaíso Metro system in Chile, serving the coastal city of Viña del Mar.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e5657e881909f16ca58352c50da |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15e2cfc908190b3489228a1997f45 |
completed | April 29, 2026, 1:26 a.m. |
Created at: April 16, 2026, 8:50 p.m.