Triple
T26519584
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Foolish Wives |
E669914
|
entity |
| Predicate | hasFilmCensorshipHistory |
P17134
|
FINISHED |
| Object | subject to cuts by studio and censors |
—
|
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: subject to cuts by studio and censors | Statement: [Foolish Wives, hasFilmCensorshipHistory, subject to cuts by studio and censors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmCensorshipHistory Context triple: [Foolish Wives, hasFilmCensorshipHistory, subject to cuts by studio and censors]
-
A.
filmCensorshipStatus
Indicates the censorship classification or restriction level that has been officially applied to a film.
-
B.
hasCensorshipHistory
chosen
Indicates that an entity has previously been subject to censorship or involved in acts of censoring content.
-
C.
hasCensorshipControversy
Indicates that an entity has been involved in disputes, criticism, or public debate related to censorship of its content or activities.
-
D.
countryOfCensorshipControversy
Indicates the country in which a particular censorship-related controversy or dispute took place.
-
E.
hasPartInFilmHistory
Indicates that an entity has played a role or contributed in some way to the history or development of film.
- 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_69eeb31b6dcc8190b30632dc3928a0c0 |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f73ae120bc8190bff94d38d7a7a00d |
completed | May 3, 2026, 12:09 p.m. |
| PD | Predicate disambiguation | batch_69f73a38d0848190aa5139144b8561c6 |
completed | May 3, 2026, 12:06 p.m. |
Created at: April 27, 2026, 1:26 a.m.