Triple
T34601784
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Our Town |
E888480
|
entity |
| Predicate | fictionalTownBasedIn |
P41362
|
FINISHED |
| Object | New Hampshire |
—
|
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 Hampshire | Statement: [Our Town, fictionalTownBasedIn, New Hampshire]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalTownBasedIn Context triple: [Our Town, fictionalTownBasedIn, New Hampshire]
-
A.
hasFictionalTownBasedOn
chosen
Indicates that a fictional town is modeled on, inspired by, or derived from a specific real-world town or location.
-
B.
fictionalTownName
Indicates that the entity is associated with the name of a town that exists only in fiction rather than in the real world.
-
C.
fictionalTownFeatured
Indicates that a fictional town is prominently depicted or serves as a key setting within a work or medium.
-
D.
hasFictionalNearbyTown
Indicates that an entity is associated with a fictional town located in its vicinity or surrounding area.
-
E.
townOfFictionalSetting
Indicates that a town serves as the fictional setting or primary location where the events of a narrative work take place.
- 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_69f349d489d48190ba30e7d97c6f5ef9 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fec4cffed08190b5e5e7cc0c87493e |
completed | May 9, 2026, 5:23 a.m. |
| PD | Predicate disambiguation | batch_69fec2ea7fe08190bd751b39515f69d1 |
completed | May 9, 2026, 5:15 a.m. |
Created at: May 1, 2026, 2:03 a.m.