Triple
T37725454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zanoni |
E939704
|
entity |
| Predicate | temporalContextInStory |
P11197
|
FINISHED |
| Object | French Revolution |
—
|
NE NERFINISHED |
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: French Revolution | Statement: [Zanoni, temporalContextInStory, French Revolution]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: temporalContextInStory Context triple: [Zanoni, temporalContextInStory, French Revolution]
-
A.
storyTimeContext
Indicates the situational setting or circumstances in which a story is told or takes place, such as time, place, or surrounding conditions.
-
B.
timeOfNarrative
chosen
Indicates the specific time or period during which the events of a narrative are set or unfold.
-
C.
narrativeRoleContext
Indicates the contextual narrative function or role an entity plays within a story or discourse (e.g., protagonist, antagonist, narrator) relative to other elements.
-
D.
timeTravelExperimentDateInStory
Indicates the date on which a time travel experiment occurs within the narrative timeline of the story.
-
E.
recontextualizesStoriesFrom
Indicates that one entity takes stories originating from another entity and presents or interprets them in a new or altered context.
- 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_69f76edc208c8190bc8b9683f75e1024 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbb9e8108c8190ae1c7940b1677e95 |
completed | May 6, 2026, 10 p.m. |
| PD | Predicate disambiguation | batch_69fbb141605c8190b9c27d70352522db |
completed | May 6, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:18 p.m.