Triple
T605893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Super Bowl LII |
E11592
|
entity |
| Predicate | totalPointsScored |
P17024
|
FINISHED |
| Object | 74 |
—
|
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: 74 | Statement: [Super Bowl LII, totalPointsScored, 74]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalPointsScored Context triple: [Super Bowl LII, totalPointsScored, 74]
-
A.
JetsScore
Indicates that the team named Jets scores a certain number of points in a game or event.
-
B.
numberOfGoals
Indicates the total count of goals scored or achieved by an entity in a given context.
-
C.
touchdownsScored
Indicates the number of touchdowns that an entity has scored.
-
D.
scoredOverPointsCareer
Indicates that an entity (typically an athlete) accumulated more than a specified number of points over the course of their entire career.
-
E.
hasScoredFor
Indicates that one entity has scored points, goals, or similar achievements on behalf of another entity, such as a team, organization, or side.
- 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_69a4932779b881908688590d59c71900 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49df34abc8190a578c8c2ab3d28e4 |
completed | March 1, 2026, 8:13 p.m. |
| PD | Predicate disambiguation | batch_69a49cf8fc1c81908a9c7df552aa1a59 |
completed | March 1, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69a49def31ec81909dc53e70f4a36eda |
completed | March 1, 2026, 8:13 p.m. |
Created at: March 1, 2026, 7:35 p.m.