Triple
T11942897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brigadeiro metro station |
E284220
|
entity |
| Predicate | line |
P1293
|
FINISHED |
| Object | Line 2–Green |
E282986
|
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: Line 2–Green | Statement: [Brigadeiro metro station, line, Line 2–Green]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 2–Green Context triple: [Brigadeiro metro station, line, Line 2–Green]
-
A.
Line 2–Green
chosen
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.
-
B.
Line 2 (Blue Line)
Line 2 (Blue Line) is one of the main lines of the Mexico City Metro system, running on a north–south axis through several key central and residential areas.
-
C.
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.
-
D.
Line 3–Red
Line 3–Red is one of the busiest and most important lines of the São Paulo Metro, running east–west across the city and connecting key residential and commercial areas.
-
E.
LRT Line 2
LRT Line 2 is an elevated rapid transit line in Metro Manila, Philippines, running east–west and serving major areas including Quezon City, Manila, and Pasig.
- 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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90342bb908190a019ac91a2b82f3d |
completed | April 10, 2026, 2:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f471ba7fd88190909596e6e01e8714 |
completed | May 1, 2026, 9:26 a.m. |
Created at: April 8, 2026, 9:45 p.m.