Triple
T15971223
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2016 MTV Video Music Awards |
E387327
|
entity |
| Predicate | mostNominationsNumber |
P8123
|
FINISHED |
| Object | 11 |
—
|
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: 11 | Statement: [2016 MTV Video Music Awards, mostNominationsNumber, 11]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mostNominationsNumber Context triple: [2016 MTV Video Music Awards, mostNominationsNumber, 11]
-
A.
mostNominationsCount
chosen
Indicates the highest number of nominations that any entity in the relevant set has received.
-
B.
mostNominationsFilm
Indicates that a film holds the highest number of nominations within a given set, context, or award event.
-
C.
mostNominationsRecipient
Indicates that the subject is the entity that has received the highest number of nominations within a given context or set.
-
D.
academyAwardsNominationsCount
Indicates the number of times an entity has been nominated for an Academy Award.
-
E.
tonyNominationsCount
Indicates the number of Tony Award nominations an entity has received.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142d6fb588190b4176eab4bbae774 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:54 a.m.