Triple
T21052238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lima Metro Line 1 |
E518615
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Gamarra 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: Gamarra station | Statement: [Lima Metro Line 1, hasStation, Gamarra station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gamarra station Context triple: [Lima Metro Line 1, hasStation, Gamarra station]
-
A.
Gamarra station
chosen
Gamarra station is a rapid transit stop on Line 1 of the Lima Metro serving the busy commercial district of Gamarra in La Victoria, Lima, Peru.
-
B.
Angamos station
Angamos station is a passenger stop on Line 1 of the Lima Metro system in Lima, Peru.
-
C.
Piedras station
Piedras station is a stop on Buenos Aires’ historic Line A subway, serving the central Monserrat area near the city’s Plaza de Mayo.
-
D.
Mirador station
Mirador station is a stop on Santiago, Chile’s Metro system, serving passengers on Line 5 in the city’s urban transit network.
-
E.
Guelatao station
Guelatao station is a Mexico City Metro station in the eastern part of the city, serving passengers on the system’s Line A.
- 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_69e0b5053ac48190921529544959e906 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fd7cabe881909e6b258a14d501a6 |
completed | April 21, 2026, 4:30 a.m. |
Created at: April 16, 2026, 2:35 p.m.