Triple
T33680992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kodachrome |
E862896
|
entity |
| Predicate | hasCensorshipReason |
P42543
|
FINISHED |
| Object | mentions a commercial product name |
—
|
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: mentions a commercial product name | Statement: [Kodachrome, hasCensorshipReason, mentions a commercial product name]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCensorshipReason Context triple: [Kodachrome, hasCensorshipReason, mentions a commercial product name]
-
A.
hasCensorshipIssue
Indicates that an entity is subject to, involved in, or associated with censorship or censorship-related concerns.
-
B.
censorshipReason
chosen
Indicates the justification or cause given for why certain content is suppressed, restricted, or removed.
-
C.
hasCensorshipHistory
Indicates that an entity has previously been subject to censorship or involved in acts of censoring content.
-
D.
wasCensored
Indicates that an entity’s content, expression, or communication was suppressed, altered, or restricted by an authority or controlling party.
-
E.
hasCensorshipTitle
Indicates that an entity has been assigned a specific title or designation for censorship or regulatory control purposes.
- 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_69f34985885c8190914322f492e04703 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fb563aec448190875410fb1a3ed624 |
completed | May 6, 2026, 2:54 p.m. |
| PD | Predicate disambiguation | batch_69fb35b9ede881908aaae93a215525df |
completed | May 6, 2026, 12:36 p.m. |
Created at: May 1, 2026, 1:43 a.m.