Triple
T3768628
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schneider Electric Marathon de Paris |
E82740
|
entity |
| Predicate | hasAidStations |
P51011
|
FINISHED |
| Object | water and nutrition points along the course |
—
|
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: water and nutrition points along the course | Statement: [Schneider Electric Marathon de Paris, hasAidStations, water and nutrition points along the course]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAidStations Context triple: [Schneider Electric Marathon de Paris, hasAidStations, water and nutrition points along the course]
-
A.
hasFocalStations
Indicates that an entity is associated with one or more primary or central stations that serve as its main points of focus or operation.
-
B.
hasEndpointStation
Indicates that something (such as a route, line, or service) has a specific station as one of its terminal endpoints.
-
C.
hasRailStation
Indicates that one entity possesses, contains, or is served by a rail station.
-
D.
hasBusStation
Indicates that a place or area contains or is served by a bus station.
-
E.
hasMetroStations
Indicates that a place or area is served by one or more metro (subway) stations.
- 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_69ad8b207b0081909d2b48843fbd8795 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcc2d4b848190bf63fb3ed5d3b2d9 |
completed | March 8, 2026, 7:21 p.m. |
| PD | Predicate disambiguation | batch_69adc04ec36c8190bd5b944d4f4d32aa |
completed | March 8, 2026, 6:30 p.m. |
| PDg | Predicate description generation | batch_69adc133ef50819094c2b971f31f1615 |
completed | March 8, 2026, 6:34 p.m. |
Created at: March 8, 2026, 3:35 p.m.