Triple
T14841171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Linas-Marcoussis |
E348966
|
entity |
| Predicate | hasSportsInfrastructure |
P114870
|
FINISHED |
| Object | training center |
—
|
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: training center | Statement: [Linas-Marcoussis, hasSportsInfrastructure, training center]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSportsInfrastructure Context triple: [Linas-Marcoussis, hasSportsInfrastructure, training center]
-
A.
hasSportsGround
Indicates that an entity possesses, includes, or is associated with a sports ground or athletic field as part of its facilities or area.
-
B.
hasSportsClubs
Indicates that an entity possesses, hosts, or is associated with one or more sports clubs.
-
C.
hasSportsPrecinct
chosen
Indicates that an entity includes, contains, or is associated with a designated area or complex specifically intended for sports activities or facilities.
-
D.
hasProfessionalSportsVenue
Indicates that one entity possesses or hosts a venue specifically used for professional sports events.
-
E.
hasSportsVenueType
Indicates that a sports venue is classified as being of a specific type or category (e.g., stadium, arena, court).
- 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_69d822ec69008190a9232caa68836872 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded28e40f08190b309d8ac6404d2fc |
completed | April 14, 2026, 11:49 p.m. |
| PD | Predicate disambiguation | batch_69de8c13418c819088ff9905ace1416a |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:53 a.m.