Triple
T10905286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas A&M vs LSU |
E257552
|
entity |
| Predicate | televisionExposure |
P2341
|
FINISHED |
| Object | nationally televised games |
—
|
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: nationally televised games | Statement: [Texas A&M vs LSU, televisionExposure, nationally televised games]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: televisionExposure Context triple: [Texas A&M vs LSU, televisionExposure, nationally televised games]
-
A.
televisionExposureLevel
chosen
Indicates the degree or amount of exposure an entity has to television content.
-
B.
televisionShare
Indicates the proportion of total television audience or viewing time captured by a particular program, channel, or entity relative to all viewing in a given market or period.
-
C.
televisionAspect
Indicates the aspect ratio or format characteristics of a television display in relation to its width and height proportions.
-
D.
televisionCategory
Indicates the classification or genre category assigned to a television-related entity (such as a show, channel, or program).
-
E.
televisionNotability
Indicates that an entity is notable or significant specifically for its involvement in television (e.g., TV roles, appearances, or contributions).
- 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_69d6aa8550c8819095508a2ed9acf3db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770656c288190828e71600bb0acd4 |
completed | April 9, 2026, 9:24 a.m. |
| PD | Predicate disambiguation | batch_69d70d3d69e08190bb369e9a7927142c |
completed | April 9, 2026, 2:21 a.m. |
Created at: April 8, 2026, 9:22 p.m.