Triple
T28548076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Austrian Federal Theatres |
E722500
|
entity |
| Predicate | hasPrimaryCityOfOperation |
P14306
|
FINISHED |
| Object | Vienna |
—
|
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: Vienna | Statement: [Austrian Federal Theatres, hasPrimaryCityOfOperation, Vienna]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPrimaryCityOfOperation Context triple: [Austrian Federal Theatres, hasPrimaryCityOfOperation, Vienna]
-
A.
notableCityOfOperation
chosen
Indicates that a city is a primary or particularly significant location where an entity conducts its operations or activities.
-
B.
hasPrimaryTheatreOfWar
Indicates that an armed conflict or military operation is chiefly conducted within a particular geographic theatre or region of war.
-
C.
hasBaseOfOperations
Indicates that an entity uses a particular location as its primary place of operation or activity.
-
D.
primaryIslandOfOperation
Indicates that an entity mainly conducts its activities or operations on a specified island.
-
E.
hadMajorOperationsIn
Indicates that an entity has undergone significant or primary operations or activities in a specified location or 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_69f01a5e42348190b1ffbca26e739c84 |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69fedfd913f48190bdcd450980868d9a |
completed | May 9, 2026, 7:18 a.m. |
| PD | Predicate disambiguation | batch_69fedf58c6e88190821a7156054c9086 |
completed | May 9, 2026, 7:16 a.m. |
Created at: April 28, 2026, 3:40 a.m.