Triple
T37012789
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Royal Conservatory |
E915992
|
entity |
| Predicate | partOfFictionalCityDistrict |
P37938
|
FINISHED |
| Object | Upper Aventa District |
—
|
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: Upper Aventa District | Statement: [Royal Conservatory, partOfFictionalCityDistrict, Upper Aventa District]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partOfFictionalCityDistrict Context triple: [Royal Conservatory, partOfFictionalCityDistrict, Upper Aventa District]
-
A.
partOfFictionalCity
Indicates that one entity is a component, area, or subdivision within a larger fictional city.
-
B.
partOfFictionalCityInfrastructure
Indicates that something is a component or subsystem within the constructed infrastructure of a fictional city.
-
C.
inCityDistrict
Indicates that one entity is located within the boundaries of a specific city district.
-
D.
regionOfCity
chosen
Indicates that a specified area or district is a constituent part or subdivision of a particular city.
-
E.
targetCityDistrict
Indicates that one entity is a specific city district that serves as the target or destination in relation to another entity.
- 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_69f76e90ed548190b187d2475f5c807d |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a013a251b4081908b43d85dd95586c5 |
completed | May 11, 2026, 2:08 a.m. |
| PD | Predicate disambiguation | batch_6a0137e4ea988190812173a5ff044098 |
completed | May 11, 2026, 1:59 a.m. |
Created at: May 3, 2026, 4:14 p.m.