Triple
T33477791
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bennet |
E857373
|
entity |
| Predicate | fictionalResidenceRegion |
P125629
|
FINISHED |
| Object | Hertfordshire |
—
|
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: Hertfordshire | Statement: [Bennet, fictionalResidenceRegion, Hertfordshire]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalResidenceRegion Context triple: [Bennet, fictionalResidenceRegion, Hertfordshire]
-
A.
fictionalResidence
Indicates that one entity is the place where another entity lives or is based within a fictional or imaginary context.
-
B.
stateOfFictionalResidence
chosen
Indicates the state or region in which a fictional character’s residence is located.
-
C.
settingOfFictionalResidence
Indicates that a location serves as the setting or backdrop for a fictional residence within a narrative work.
-
D.
cityOfFictionalResidence
Indicates that a fictional character or entity resides in, or is associated with living in, a particular city within a narrative or fictional context.
-
E.
fictionalSettingRegion
Indicates that a fictional setting is located within or associated with a specific geographic or administrative region.
- 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_69f3497472508190b300ebd3fd402367 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fe72dca2f08190beff17de3d2aada6 |
completed | May 8, 2026, 11:33 p.m. |
| PD | Predicate disambiguation | batch_69fe70bca8d08190b810e1e616ceac44 |
completed | May 8, 2026, 11:24 p.m. |
Created at: May 1, 2026, 1:38 a.m.