Triple
T31498499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple Design Awards 2021 |
E803611
|
entity |
| Predicate | numberOfWinningAppsAndGames |
P203054
|
FINISHED |
| Object | 12 |
—
|
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: 12 | Statement: [Apple Design Awards 2021, numberOfWinningAppsAndGames, 12]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfWinningAppsAndGames Context triple: [Apple Design Awards 2021, numberOfWinningAppsAndGames, 12]
-
A.
numberOfWins
Indicates the count of times an entity has achieved victory in a relevant context or competition.
-
B.
gamesWonBy
Indicates the number of games that have been won by a particular entity in a given context.
-
C.
mostGamesWonBy
Indicates that one entity holds the record for having won the greatest number of games compared to others in a given context.
-
D.
numberOfReleasedGames
Indicates the total count of games that have been released by or associated with a given entity.
-
E.
numberOfGamesDepicted
Indicates the total count of distinct games that are shown or represented in the given context.
- 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_69f348cae52081909fa8e5f697523ae3 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a0119132e848190820a688d139fbf75 |
completed | May 10, 2026, 11:47 p.m. |
| PD | Predicate disambiguation | batch_6a01188dfdec8190b7f675264a281733 |
completed | May 10, 2026, 11:45 p.m. |
| PDg | Predicate description generation | batch_6a0119127ca481909e921e7d95716b00 |
completed | May 10, 2026, 11:47 p.m. |
Created at: April 30, 2026, 9:42 p.m.