Triple
T1216218
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thelma Ritter |
E26111
|
entity |
| Predicate | numberOfAcademyAwardNominations |
P6104
|
FINISHED |
| Object | 6 |
—
|
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: 6 | Statement: [Thelma Ritter, numberOfAcademyAwardNominations, 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAcademyAwardNominations Context triple: [Thelma Ritter, numberOfAcademyAwardNominations, 6]
-
A.
academyAwardNominations
chosen
Indicates that an entity has received one or more nominations for an Academy Award (Oscars).
-
B.
numberOfAcademyAwardsForBestActress
Indicates the total count of Academy Awards received by an entity specifically in the Best Actress category.
-
C.
mostNominationsCount
Indicates the highest number of nominations that any entity in the relevant set has received.
-
D.
mostNominationsFilm
Indicates that a film holds the highest number of nominations within a given set, context, or award event.
-
E.
numberOfAwards
Indicates the total count of awards that have been received by an entity.
- 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_69a4948331fc8190b531ac9bec71c491 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4be059c5c8190a200f09442c22334 |
completed | March 1, 2026, 10:30 p.m. |
| PD | Predicate disambiguation | batch_69a4bb62a7c08190a79dcb6ff72ac99b |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:46 p.m.