Triple
T31656275
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New New York City |
E807865
|
entity |
| Predicate | replacesInFiction |
P177116
|
FINISHED |
| Object | New York City |
—
|
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: New York City | Statement: [New New York City, replacesInFiction, New York City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: replacesInFiction Context triple: [New New York City, replacesInFiction, New York City]
-
A.
replacesInNarrative
Indicates that one narrative element takes the place of another within the structure or progression of a story.
-
B.
laterSettingOfFiction
Indicates that one fictional work is set chronologically later than another within a shared narrative or story world.
-
C.
fictionalStandInFor
Indicates that one entity serves as a fictional or symbolic substitute representing another real or implied entity.
-
D.
replacesFictionalLocation
chosen
Indicates that one fictional location takes the place of, or is used in lieu of, another fictional location within a narrative or setting.
-
E.
createsInFiction
Indicates that one entity is the creator or originator of another entity within a fictional or narrative 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_69f348daf95c81908b4c985b7ddcd0b3 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fd2cf39b0c8190811b8a6fa9410560 |
completed | May 8, 2026, 12:23 a.m. |
| PD | Predicate disambiguation | batch_69fd2ad8dd988190a9899701ba00d917 |
completed | May 8, 2026, 12:14 a.m. |
Created at: April 30, 2026, 10:55 p.m.