Triple
T37435291
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 6 (Paris Métro) |
E930245
|
entity |
| Predicate | hasAverageDistanceBetweenStations |
P187815
|
FINISHED |
| Object | approximately 500 metres |
—
|
LITERAL 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: approximately 500 metres | Statement: [Line 6 (Paris Métro), hasAverageDistanceBetweenStations, approximately 500 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAverageDistanceBetweenStations Context triple: [Line 6 (Paris Métro), hasAverageDistanceBetweenStations, approximately 500 metres]
-
A.
numberOfStations
Indicates the total count of stations associated with or contained by a given entity.
-
B.
numberOfUndergroundStations
Indicates the total count of underground (subway/metro) stations associated with a given entity.
-
C.
hasMajorRailLinksTo
Indicates that there are significant railway connections or routes between two locations.
-
D.
distanceFromStation
Indicates the measured spatial separation between an entity and a specified station.
-
E.
stopsAtFewerStationsThan
Indicates that one transit service or route makes stops at a smaller number of stations than another transit service or route.
- 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_69f76ebfdcb8819098562ff3db673b04 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fb92efc5948190a040ba2028bab964 |
completed | May 6, 2026, 7:13 p.m. |
| PD | Predicate disambiguation | batch_69fb8d0b52588190bb29937a43b99b5e |
completed | May 6, 2026, 6:48 p.m. |
| PDg | Predicate description generation | batch_69fb92ee27408190b0116ef2d789abac |
completed | May 6, 2026, 7:13 p.m. |
Created at: May 3, 2026, 4:17 p.m.