Triple
T1280716
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metro-Goldwyn-Mayer |
E27316
|
entity |
| Predicate | hasFilmLibrarySize |
P425
|
FINISHED |
| Object | thousands of film titles |
—
|
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: thousands of film titles | Statement: [Metro-Goldwyn-Mayer, hasFilmLibrarySize, thousands of film titles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmLibrarySize Context triple: [Metro-Goldwyn-Mayer, hasFilmLibrarySize, thousands of film titles]
-
A.
librarySystemSize
Indicates the overall scale or capacity of a library system, such as the number of branches, items, or resources it encompasses.
-
B.
typicalNumberOfSelectedFilms
Indicates the usual or average number of films that are chosen or selected in a given context or process.
-
C.
collectionSize
chosen
Indicates the total number of items contained within a specified collection.
-
D.
numberOfVolumes
Indicates the total count of separate volumes or parts that make up a multi-volume work or collection.
-
E.
hasNumberOfCinemas
Indicates the quantity of cinemas associated with a given 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_69a496d3710c8190955dee8bc0dacb50 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c094eb4881909a33061339f91190 |
completed | March 1, 2026, 10:41 p.m. |
| PD | Predicate disambiguation | batch_69a4bee276d8819092f71c5a1140bb61 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:50 p.m.