Triple
T34010649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Betty Bold |
E872100
|
entity |
| Predicate | fictionalResidenceType |
P197778
|
FINISHED |
| Object | suburban house |
—
|
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: suburban house | Statement: [Betty Bold, fictionalResidenceType, suburban house]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalResidenceType Context triple: [Betty Bold, fictionalResidenceType, suburban house]
-
A.
fictionalResidence
Indicates that one entity is the place where another entity lives or is based within a fictional or imaginary context.
-
B.
settingOfFictionalResidence
Indicates that a location serves as the setting or backdrop for a fictional residence within a narrative work.
-
C.
stateOfFictionalResidence
Indicates the state or region in which a fictional character’s residence is located.
-
D.
fictionalBuilding
Indicates that a building is imaginary or exists only within a fictional or invented context.
-
E.
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.
- 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_69f349a08848819084b348d64c1879c3 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69feaa483fcc81909d8a46b38a8717bf |
completed | May 9, 2026, 3:30 a.m. |
| PD | Predicate disambiguation | batch_69fea8c9d45c81908ccc8619e5fefac1 |
completed | May 9, 2026, 3:23 a.m. |
| PDg | Predicate description generation | batch_69feaa477f7c81909382b3aa77e7e11c |
completed | May 9, 2026, 3:30 a.m. |
Created at: May 1, 2026, 1:51 a.m.