Triple
T11942925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brigadeiro metro station |
E284220
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Line 2–Green trunk section |
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 trunk section | Statement: [Brigadeiro metro station, partOf, Line 2–Green trunk section]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 2–Green trunk section Context triple: [Brigadeiro metro station, partOf, Line 2–Green trunk section]
-
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.
Line 2
Line 2 is a major route of the Tunis Metro light rail network, serving key districts within the Tunis metropolitan area.
-
D.
Line 2
Line 2 is one of the two automated light metro lines of the Lille Metro system in northern France, serving numerous stations across the metropolitan area.
-
E.
Line 2
Line 2 is a major east–west rapid transit route of the Shanghai Metro that connects key commercial, residential, and airport hubs across the city.
- 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_69f440a5a9c8819086a94ad60c6881b8 |
completed | May 1, 2026, 5:56 a.m. |
Created at: April 8, 2026, 9:45 p.m.