Triple
T31656292
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New New York City |
E807865
|
entity |
| Predicate | hasInhabitantsInFiction |
P97696
|
FINISHED |
| Object | humans |
—
|
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: humans | Statement: [New New York City, hasInhabitantsInFiction, humans]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInhabitantsInFiction Context triple: [New New York City, hasInhabitantsInFiction, humans]
-
A.
hasFictionalInhabitants
chosen
Indicates that a place or setting is inhabited by fictional or imaginary beings.
-
B.
livesInFiction
Indicates that one entity exists or resides within the fictional world or narrative setting created by another entity.
-
C.
hasSpeciesInFiction
Indicates that a fictional work or universe features a particular species as part of its narrative or setting.
-
D.
hasFictionalDepictions
Indicates that an entity is represented or portrayed in one or more fictional works or narratives.
-
E.
hasNotableResidentInFiction
Indicates that a place or entity is notably associated with a fictional character who is depicted as residing there.
- 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_69ff5f5ecc808190b2df364da108ff4c |
completed | May 9, 2026, 4:22 p.m. |
| PD | Predicate disambiguation | batch_69ff5b84131c8190bf81d7fb53e934bc |
completed | May 9, 2026, 4:06 p.m. |
Created at: April 30, 2026, 10:55 p.m.