Triple
T24234271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Riponne–Maurice Béjart metro station |
E601829
|
entity |
| Predicate | hasAdjacentStationOnLineM2 |
P155289
|
FINISHED |
| Object | Lausanne–Flon metro 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: Lausanne–Flon metro station | Statement: [Riponne–Maurice Béjart metro station, hasAdjacentStationOnLineM2, Lausanne–Flon metro station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAdjacentStationOnLineM2 Context triple: [Riponne–Maurice Béjart metro station, hasAdjacentStationOnLineM2, Lausanne–Flon metro station]
-
A.
hasAdjacentStationOnLine1
Indicates that one station is directly next to another station along Line 1 in the network.
-
B.
adjacentStationOnLineM2
Indicates that two stations are directly next to each other as consecutive stops on metro line M2.
-
C.
hasAdjacentStationOnLine 2
Indicates that one station is directly next to another station along Line 2 in the network.
-
D.
hasAdjacentStationOnLine12
Indicates that one station is directly next to another station along transit line 12, with no other stations in between on that line.
-
E.
hasAdjacentStationOnM
Indicates that one station is directly next to another station along metro line M, with no other stations in between.
- F. None of above. chosen
Provenance (4 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_69e29538aafc8190a2386fdebbd1393b |
completed | April 17, 2026, 8:16 p.m. |
| NER | Named-entity recognition | batch_69f28a99889c819080efdec76be04646 |
completed | April 29, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69f1c448abec8190b87cbf9ed419a309 |
completed | April 29, 2026, 8:41 a.m. |
| PDg | Predicate description generation | batch_69f1c6d4e99081909f61899eccafb73e |
completed | April 29, 2026, 8:52 a.m. |
Created at: April 18, 2026, 12:02 a.m.