Triple
T4413393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NCAA men’s cross country |
E94904
|
entity |
| Predicate | nonScoringDisplacersCount |
P55507
|
FINISHED |
| Object | 2 runners |
—
|
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: 2 runners | Statement: [NCAA men’s cross country, nonScoringDisplacersCount, 2 runners]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nonScoringDisplacersCount Context triple: [NCAA men’s cross country, nonScoringDisplacersCount, 2 runners]
-
A.
dropCount
Indicates the number of times an entity has been dropped or caused to drop something.
-
B.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
C.
numberOfDiscs
Indicates the quantity of discs associated with or contained by a given entity.
-
D.
numberOfNoContests
Indicates the total count of events, matches, or decisions that were recorded as "no contest" rather than a win, loss, or draw.
-
E.
scoreUsedFor
Indicates that a particular score or rating is used for a specific purpose, decision, or downstream process.
- 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_69b34539638c8190abfea3eb29425210 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b354e7b30c819082ee781dd202dcc4 |
completed | March 13, 2026, 12:05 a.m. |
| PD | Predicate disambiguation | batch_69b34f5d0c54819085c08533bb58030a |
completed | March 12, 2026, 11:42 p.m. |
| PDg | Predicate description generation | batch_69b34ff7018c81908ad8597e525c042b |
completed | March 12, 2026, 11:44 p.m. |
Created at: March 12, 2026, 11:29 p.m.