Triple
T20157858
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metro de la Ciudad de México |
E491620
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object | Línea 2 |
—
|
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: Línea 2 | Statement: [Metro de la Ciudad de México, hasLine, Línea 2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Línea 2 Context triple: [Metro de la Ciudad de México, hasLine, Línea 2]
-
A.
Línea 2
Línea 2 is a metro line that forms part of an urban rapid transit network and connects with other lines, including Línea 6, at designated interchange stations.
-
B.
Metro Line 2
chosen
Metro Line 2 is one of the main lines of the Mexico City Metro system, running north–south through key residential and commercial areas and serving as a major transit corridor for daily commuters.
-
C.
Línea 3
Línea 3 is a metro line that forms part of an urban rapid transit network, connecting multiple stations and intersecting with other lines to facilitate passenger transfers.
-
D.
Line 2–Green
Line 2–Green is a major rapid transit line of the São Paulo Metro system, serving key central and eastern districts of São Paulo, Brazil.
-
E.
Ligne 2
Ligne 2 is a Paris Métro line forming a semicircular route through northern Paris, known for serving major districts such as Pigalle and Belleville.
- 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_69da6265f8f0819080b29c752a574088 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e667e18a0c8190a2cc2b305da28047 |
completed | April 20, 2026, 5:52 p.m. |
Created at: April 11, 2026, 11:34 p.m.