Triple
T10781780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Poor Little Rich Girl |
E254334
|
entity |
| Predicate | hasFilmCensorshipIssues |
P74007
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Poor Little Rich Girl, hasFilmCensorshipIssues, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmCensorshipIssues Context triple: [Poor Little Rich Girl, hasFilmCensorshipIssues, yes]
-
A.
censorshipIssues
Indicates that one entity imposes restrictions, suppression, or control over the information, expression, or content associated with another entity.
-
B.
wasCensored
chosen
Indicates that an entity’s content, expression, or communication was suppressed, altered, or restricted by an authority or controlling party.
-
C.
censorshipReason
Indicates the justification or cause given for why certain content is suppressed, restricted, or removed.
-
D.
hasCensoredVersion
Indicates that one entity is a version of another in which certain content has been removed, obscured, or altered to comply with censorship requirements.
-
E.
hasCensorshipHistory
Indicates that an entity has previously been subject to censorship or involved in acts of censoring content.
- 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_69d6aa609f008190a294200aefcb7bd5 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d732c5618081908f72838ed42ed05c |
completed | April 9, 2026, 5:01 a.m. |
| PD | Predicate disambiguation | batch_69d6f31455648190b5c24690487b1b54 |
completed | April 9, 2026, 12:30 a.m. |
Created at: April 8, 2026, 9:17 p.m.