Triple
T16347017
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Place de Bretagne (Nantes) |
E396957
|
entity |
| Predicate | hasNearbyBusLines |
P15438
|
FINISHED |
| Object | multiple TAN bus lines (Nantes) |
—
|
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: multiple TAN bus lines (Nantes) | Statement: [Place de Bretagne (Nantes), hasNearbyBusLines, multiple TAN bus lines (Nantes)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyBusLines Context triple: [Place de Bretagne (Nantes), hasNearbyBusLines, multiple TAN bus lines (Nantes)]
-
A.
hasNearbyTramStop
Indicates that a location has a tram stop situated within a short walking distance or close proximity.
-
B.
operatorOfNearbyTransit
Indicates that an entity operates or manages a public transit service located in close geographic proximity to another specified entity.
-
C.
hasAdjacentBusStation
Indicates that one location has a bus station situated directly next to or very near it.
-
D.
hasStopNear
Indicates that one entity has a stop or stopping point located in close proximity to another entity.
-
E.
hasPublicTransportStop
chosen
Indicates that a location or area contains or is served by a public transport stop, such as a bus, tram, or train stop.
- F. None of above.
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_69d87f26864c819088365ca381a003c2 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2da1038d88190b8292cfe71bc4f2a |
completed | April 18, 2026, 1:10 a.m. |
| PD | Predicate disambiguation | batch_69e226eba9b48190af6e80d3d1c2aed3 |
completed | April 17, 2026, 12:26 p.m. |
Created at: April 10, 2026, 5:07 a.m.