Triple
T33344500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Republic of Zubrowka |
E853758
|
entity |
| Predicate | hasFictionalHotel |
P199001
|
FINISHED |
| Object | Grand Budapest Hotel |
—
|
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: Grand Budapest Hotel | Statement: [Republic of Zubrowka, hasFictionalHotel, Grand Budapest Hotel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalHotel Context triple: [Republic of Zubrowka, hasFictionalHotel, Grand Budapest Hotel]
-
A.
hasFictionalPub
Indicates that an entity features or includes a fictional pub as part of its content, setting, or structure.
-
B.
hasFictionalProprietor
Indicates that something is owned, managed, or run by a fictional character or entity within a narrative context.
-
C.
hasFictionalDiner
Indicates that one entity features or includes a fictional diner associated with another entity.
-
D.
hasFictionalLandmark
Indicates that one entity includes, features, or is associated with a landmark that is fictional rather than real.
-
E.
hasFictionalProperty
Indicates that an entity possesses a property, attribute, or characteristic that exists only in a fictional or imaginary 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_69f3496a1a588190bad9cbe9221144e0 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff1a972bf08190860696ffcd887c0f |
completed | May 9, 2026, 11:29 a.m. |
| PD | Predicate disambiguation | batch_69ff184005d88190bf38283ebc499b28 |
completed | May 9, 2026, 11:19 a.m. |
| PDg | Predicate description generation | batch_69ff1a95b6fc81909331bb03e263b5b9 |
completed | May 9, 2026, 11:29 a.m. |
Created at: May 1, 2026, 1:34 a.m.