Triple
T34949245
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Hanks as adult Josh Baskin |
E1007941
|
entity |
| Predicate | cityOfStory |
P81759
|
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: [Tom Hanks as adult Josh Baskin, cityOfStory, New York City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cityOfStory Context triple: [Tom Hanks as adult Josh Baskin, cityOfStory, New York City]
-
A.
cityOfFictionalLocation
Indicates that a fictional location is situated within or associated with a particular city.
-
B.
townOfFictionalSetting
Indicates that a town serves as the fictional setting or primary location where the events of a narrative work take place.
-
C.
homeCityInStory
chosen
Indicates that a specified city serves as a character’s home city within the context of a particular story.
-
D.
cityOfFictionalActivity
Indicates that a fictional activity, event, or storyline takes place in the specified city.
-
E.
protagonistCity
Indicates the city in which the main character or protagonist of a work is primarily based or associated.
- 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_69f76dc5d4308190b77553ee07b1ede6 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78c61ed4c8190ad84c918fa9af55a |
completed | May 3, 2026, 5:56 p.m. |
| PD | Predicate disambiguation | batch_69f78b8cb3a881909ebaac1b503988c2 |
completed | May 3, 2026, 5:53 p.m. |
Created at: May 3, 2026, 4 p.m.