Triple
T12016330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NFL Championship |
E286034
|
entity |
| Predicate | numberOfTitlesGreenBayPackers |
P5808
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [NFL Championship, numberOfTitlesGreenBayPackers, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTitlesGreenBayPackers Context triple: [NFL Championship, numberOfTitlesGreenBayPackers, 8]
-
A.
teamGreenBayDivision
Indicates that a team is in the same division as the Green Bay football team within a sports league.
-
B.
team1SuperBowlTitles
chosen
Indicates the number of Super Bowl championships that the first team has won.
-
C.
superBowlTitlesWith
Indicates that one entity has won a specified number of Super Bowl titles associated with another entity (such as a team or organization).
-
D.
team2SuperBowlTitles
Indicates the number of Super Bowl championships won by the second team in a given context.
-
E.
teamCoachedToSuperBowlTitles
Indicates that a coach led a specific team to one or more Super Bowl championship victories.
- 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_69d6ab45a368819084fce08bf0dc3705 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9100b4ca8819084845ca4c13e34ce |
completed | April 10, 2026, 2:58 p.m. |
| PD | Predicate disambiguation | batch_69d902b6ebbc8190b13c44a61c6f81b9 |
completed | April 10, 2026, 2:01 p.m. |
Created at: April 8, 2026, 9:47 p.m.