Triple
T671786
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Film Registry |
E12986
|
entity |
| Predicate | maximumAnnualSelections |
P12579
|
FINISHED |
| Object | 25 films per year |
—
|
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: 25 films per year | Statement: [National Film Registry, maximumAnnualSelections, 25 films per year]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumAnnualSelections Context triple: [National Film Registry, maximumAnnualSelections, 25 films per year]
-
A.
allStarSelectionCount
Indicates the number of times an entity has been selected as an All-Star.
-
B.
maximumNumberOfLaureatesPerYear
Indicates the highest allowable or observed count of laureates associated with a given year.
-
C.
typicalNumberOfNominees
chosen
Indicates the usual or standard count of nominees associated with something, such as an award, position, or selection process.
-
D.
lastAwarded
Indicates the most recent time or instance at which an entity received a particular award.
-
E.
mostNominationsCount
Indicates the highest number of nominations that any entity in the relevant set 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_69a493355dec819098d4244b2fa34885 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a1b3682c8190a9b9a454480c3446 |
completed | March 1, 2026, 8:29 p.m. |
| PD | Predicate disambiguation | batch_69a49d1a16c48190af89e3b078a4957e |
completed | March 1, 2026, 8:10 p.m. |
Created at: March 1, 2026, 7:36 p.m.