Triple
T2330791
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neve |
E44196
|
entity |
| Predicate | associatedEventType |
P38977
|
FINISHED |
| Object | multi-sport event |
—
|
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: multi-sport event | Statement: [Neve, associatedEventType, multi-sport event]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedEventType Context triple: [Neve, associatedEventType, multi-sport event]
-
A.
associatedCamp
Indicates a relationship where an entity is linked or connected to a particular camp, typically as its relevant or affiliated camp.
-
B.
attributesEventTo
Indicates assigning responsibility, origin, or cause of an event to a particular entity.
-
C.
alsoHostedEventType
Indicates that the same host was responsible for organizing or holding another event of a specified type.
-
D.
associatedConvention
Indicates a relationship where something is linked or connected to a particular convention (such as an event, standard, or formal gathering).
-
E.
associatedWithSee
Indicates a relationship where one entity is contextually or functionally linked to another through the act or concept of seeing or visual observation.
- 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_69a889132b488190bbb43ad4780ddd92 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abcc30c5e881908c5d526d7e7491d0 |
completed | March 7, 2026, 6:56 a.m. |
| PD | Predicate disambiguation | batch_69abc5926d048190a535e3f23d41de2a |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abcc2fa25c8190858c1c541b914f4c |
completed | March 7, 2026, 6:56 a.m. |
Created at: March 4, 2026, 7:51 p.m.