Triple
T3768614
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schneider Electric Marathon de Paris |
E82740
|
entity |
| Predicate | approximateParticipantsPerYear |
P1131
|
FINISHED |
| Object | over 50,000 |
—
|
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: over 50,000 | Statement: [Schneider Electric Marathon de Paris, approximateParticipantsPerYear, over 50,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateParticipantsPerYear Context triple: [Schneider Electric Marathon de Paris, approximateParticipantsPerYear, over 50,000]
-
A.
numberOfParticipants
chosen
Indicates the total count of entities involved in a particular event, activity, or relationship.
-
B.
approximateAudienceSize
Indicates an estimated number of individuals or entities that are expected to be reached or affected in a given context.
-
C.
typicalNumberOfRecipientsPerYear
Indicates the usual or average count of recipients involved in or affected by something within a one-year period.
-
D.
audienceSizeApproximate
Indicates an estimated or approximate number of people in the audience for an event or content.
-
E.
membershipPeakYear
Indicates the year in which an entity’s membership count reached its highest level.
- 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_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. |
Created at: March 8, 2026, 3:35 p.m.