Triple
T31681014
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Simon Wheeler |
E808536
|
entity |
| Predicate | countyInFiction |
P201473
|
FINISHED |
| Object | Calaveras County, California |
—
|
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: Calaveras County, California | Statement: [Simon Wheeler, countyInFiction, Calaveras County, California]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countyInFiction Context triple: [Simon Wheeler, countyInFiction, Calaveras County, California]
-
A.
hasFictionalCounty
Indicates that one entity includes, is set in, or is associated with a county that is fictional rather than real.
-
B.
hasFictionalCountySeatRole
Indicates that an entity serves in the role of county seat within a fictional or imaginary administrative setting.
-
C.
boroughOfFictionalSetting
Indicates that a fictional setting is located within or associated with a specific borough.
-
D.
hasFictionalNeighboringCounty
Indicates that one county is depicted as geographically adjacent to another county within a fictional or imaginary setting.
-
E.
hasFictionalTownBasedOn
Indicates that a fictional town is modeled on, inspired by, or derived from a specific real-world town or location.
- F. None of above. chosen
Provenance (4 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_69f348dcf5d48190ac25b1365ae717a8 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fff9b126b4819085a4cf8791d388d1 |
completed | May 10, 2026, 3:21 a.m. |
| PD | Predicate disambiguation | batch_69fff8f913a881908d3b7e490d92631f |
completed | May 10, 2026, 3:18 a.m. |
| PDg | Predicate description generation | batch_69fff9b0338c8190a24ed0b5dc9784b2 |
completed | May 10, 2026, 3:21 a.m. |
Created at: April 30, 2026, 11:04 p.m.