Triple
T9795145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Stop at Willoughby |
E237698
|
entity |
| Predicate | fictionalTownDescription |
P71480
|
FINISHED |
| Object | idyllic small town |
—
|
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: idyllic small town | Statement: [A Stop at Willoughby, fictionalTownDescription, idyllic small town]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalTownDescription Context triple: [A Stop at Willoughby, fictionalTownDescription, idyllic small town]
-
A.
fictionalTownName
Indicates that the entity is associated with the name of a town that exists only in fiction rather than in the real world.
-
B.
fictionalTownFeatured
Indicates that a fictional town is prominently depicted or serves as a key setting within a work or medium.
-
C.
hasFictionalTownBasedOn
Indicates that a fictional town is modeled on, inspired by, or derived from a specific real-world town or location.
-
D.
fictionalPlaceType
chosen
Indicates that a place is a fictional location and specifies what type or category of fictional place it is.
-
E.
fictionalBirthPlace
Indicates the fictional location where a character or entity is described as having been born within a narrative or imagined 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_69ca84dc04488190b9c91193976c0960 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda34916dc8190acef2ba003e56a33 |
completed | April 1, 2026, 10:59 p.m. |
| PD | Predicate disambiguation | batch_69cd03da45a88190b71b1be3354c15a6 |
completed | April 1, 2026, 11:39 a.m. |
Created at: March 30, 2026, 8:28 p.m.