Triple
T13473296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Victoria Winters |
E318183
|
entity |
| Predicate | timePeriodOfFictionalWork |
P106751
|
FINISHED |
| Object | 1970s (in 2012 film adaptation) |
—
|
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: 1970s (in 2012 film adaptation) | Statement: [Victoria Winters, timePeriodOfFictionalWork, 1970s (in 2012 film adaptation)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timePeriodOfFictionalWork Context triple: [Victoria Winters, timePeriodOfFictionalWork, 1970s (in 2012 film adaptation)]
-
A.
fictionalTraditionDuration
Indicates the length of time a fictional tradition has existed or is observed.
-
B.
fictionalAge
Indicates the age attributed to an entity within a fictional or narrative context, rather than its real-world age.
-
C.
fictionalTime
Indicates that the associated time or temporal reference exists only within a fictional or imagined context, rather than in real-world chronology.
-
D.
fictionalTimeDepth
chosen
Indicates a relationship where an entity is associated with a time period or temporal depth that exists only within a fictional or imagined context.
-
E.
storyTimeSpanInFilm
Indicates the duration of time that the story or narrative covers within the 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_69d806b6bfec819089222715b2e86c8e |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf2447bc81908baf1f4b55095144 |
completed | April 12, 2026, 2:41 p.m. |
| PD | Predicate disambiguation | batch_69dbadfddefc81909ef7fde23b181b5c |
completed | April 12, 2026, 2:36 p.m. |
Created at: April 9, 2026, 9:42 p.m.