Triple
T5878534
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NFL officiating crews |
E130685
|
entity |
| Predicate | includesOffFieldOfficialCount |
P39657
|
FINISHED |
| Object | 1 replay official |
—
|
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: 1 replay official | Statement: [NFL officiating crews, includesOffFieldOfficialCount, 1 replay official]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesOffFieldOfficialCount Context triple: [NFL officiating crews, includesOffFieldOfficialCount, 1 replay official]
-
A.
numberOfOffFieldUmpires
Indicates the count of umpires assigned to officiate a game from positions off the field of play.
-
B.
requiresRingOfficials
Indicates that the event or activity cannot proceed or be valid unless designated ring officials are present or assigned.
-
C.
usesNumberOfPlayersOnFieldPerTeam
Indicates that the relationship specifies or depends on how many players each team has on the field at a given time.
-
D.
areCountedBy
Indicates that one entity serves as the counting mechanism, record, or process by which the quantity of another entity is determined.
-
E.
officeHolderCountIncludes
chosen
Indicates that a specified count or total explicitly includes the number of individuals holding a particular office or position.
- 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_69c0085523688190bfd487479ce819e6 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0432fea5881909f5c291dd8db6105 |
completed | March 22, 2026, 7:29 p.m. |
| PD | Predicate disambiguation | batch_69c033499ca08190bd26cee5b03f6306 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:57 p.m.