Triple
T20397489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Fifty Worst Films of All Time |
E500246
|
entity |
| Predicate | numberOfFilmsCovered |
P139980
|
FINISHED |
| Object | 50 |
—
|
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: 50 | Statement: [The Fifty Worst Films of All Time, numberOfFilmsCovered, 50]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFilmsCovered Context triple: [The Fifty Worst Films of All Time, numberOfFilmsCovered, 50]
-
A.
numberOfFilmsWorkedOn
Indicates the total count of films on which the subject has worked or participated.
-
B.
numberOfTVFilms
Indicates the total count of television films associated with a given entity.
-
C.
typicalNumberOfSelectedFilms
Indicates the usual or average number of films that are chosen or selected in a given context or process.
-
D.
numberOfFilmsAppearedIn
Indicates the total count of distinct films in which a given entity has appeared.
-
E.
numberOfFilmsProduced
Indicates the total count of films that an entity has produced.
- 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_69e0b4a81bec8190b69adfdc1336a015 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6798c2b28819092fab93f01218cde |
completed | April 20, 2026, 7:07 p.m. |
| PD | Predicate disambiguation | batch_69e5765d7cb48190adec18d6d1e3d263 |
completed | April 20, 2026, 12:42 a.m. |
| PDg | Predicate description generation | batch_69e58d7481508190a87c8b88f9df9879 |
completed | April 20, 2026, 2:20 a.m. |
Created at: April 16, 2026, 11:28 a.m.