Triple
T21847657
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charpennes – Charles Hernu |
E539416
|
entity |
| Predicate | connectsMetroLines |
P18378
|
FINISHED |
| Object | Line B |
—
|
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: Line B | Statement: [Charpennes – Charles Hernu, connectsMetroLines, Line B]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line B Context triple: [Charpennes – Charles Hernu, connectsMetroLines, Line B]
-
A.
Line B
Line B is one of the main rapid transit lines of the Medellín Metro system, serving several neighborhoods in the Aburrá Valley metropolitan area.
-
B.
Line B
chosen
Line B is one of the main routes of the Porto Metro light rail system in Porto, Portugal, connecting key suburban areas with the city center.
-
C.
Line B
Line B is a major Mexico City Metro route that runs diagonally across the city, connecting central areas with northeastern suburbs and serving as an important commuter corridor.
-
D.
Line B
Line B is one of the main lines of the Buenos Aires Underground, running through key commercial and residential areas of the city.
-
E.
Line B
Line B is one of the main tram routes in the Reims tramway network in Reims, France, providing urban public transport across key areas of the city.
- 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_69e0c476c3c88190a92d08ebb59a128a |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f0bd558ed88190a10b8d6752105cd6 |
completed | April 28, 2026, 1:59 p.m. |
Created at: April 16, 2026, 6:55 p.m.