Triple
T5901310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FIBA World Cup gold medal (head coach, 2014) |
E131231
|
entity |
| Predicate | frequencyOfCompetition |
P65013
|
FINISHED |
| Object | every four years |
—
|
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: every four years | Statement: [FIBA World Cup gold medal (head coach, 2014), frequencyOfCompetition, every four years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frequencyOfCompetition Context triple: [FIBA World Cup gold medal (head coach, 2014), frequencyOfCompetition, every four years]
-
A.
levelOfCompetition
Indicates the intensity or degree of competitive pressure present in a given context or interaction.
-
B.
frequencyOfTournament
chosen
Indicates how often a particular tournament takes place within a given time period.
-
C.
gamesFrequency
Indicates how often the related entities engage in playing games together or participate in game-related activities.
-
D.
competitionOf
Indicates a relationship where one entity is the competitive event, contest, or rivalry involving another entity.
-
E.
matchupFrequency
Indicates how often a particular pair or set of entities are matched or paired against each other within a given context or timeframe.
- 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_69c0085864a88190a569c05ff7d65f29 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0400f1af881908d376ea4793f6dea |
completed | March 22, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_69c0334dc8248190b7394dcece362d52 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:58 p.m.