Triple
T5376608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Western Conference Second Round |
E108974
|
entity |
| Predicate | temporalOccurrence |
P29111
|
FINISHED |
| Object | annually |
—
|
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: annually | Statement: [Western Conference Second Round, temporalOccurrence, annually]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: temporalOccurrence Context triple: [Western Conference Second Round, temporalOccurrence, annually]
-
A.
occurredDuring
Indicates that one event or action took place within the temporal span of another event or time period.
-
B.
eventOccurredAt
Indicates that a particular event took place at a specific time or location.
-
C.
temporalRelation
Indicates a relationship that specifies how two events or states are positioned relative to each other in time (e.g., before, after, or overlapping).
-
D.
occursOnOrAround
Indicates that an event or action takes place on a specific date or within a small, approximate time window surrounding that date.
-
E.
temporalAspect
chosen
Indicates the time-related characteristics or phase (such as duration, frequency, or temporal status) associated with an event or relationship.
- 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_69bd440c77948190aad2a5f39b7b80f5 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd88801b188190b9ac35ed89167fa3 |
completed | March 20, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69bd846172788190969f24bc7503c05e |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2:03 p.m.