Triple
T38478472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wulfburg |
E915604
|
entity |
| Predicate | belongsToFictionalCountry |
P133191
|
FINISHED |
| Object | Nazi Germany |
—
|
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: Nazi Germany | Statement: [Wulfburg, belongsToFictionalCountry, Nazi Germany]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: belongsToFictionalCountry Context triple: [Wulfburg, belongsToFictionalCountry, Nazi Germany]
-
A.
locatedInFictionalCountry
Indicates that an entity exists or is situated within a country that is fictional rather than real.
-
B.
belongsToFictionalContinent
Indicates that something is located on, associated with, or a part of a specific fictional continent within an imagined world.
-
C.
belongsToFictionalRegion
Indicates that an entity is located within, associated with, or under the jurisdiction of a fictional or imaginary geographic region.
-
D.
associatedWithCountryInFiction
chosen
Indicates a fictional relationship in which an entity is linked or connected to a particular country within a fictional context or narrative.
-
E.
countryOfOriginFictional
Indicates that a fictional work, character, or element originates from or is associated with a particular country within its narrative or setting.
- 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_69f76e8ff5cc8190a88803369183845e |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a0061454944819088a0babfe60f69fc |
completed | May 10, 2026, 10:43 a.m. |
| PD | Predicate disambiguation | batch_6a0060b9ee108190b91e8d99a16f2b30 |
completed | May 10, 2026, 10:40 a.m. |
Created at: May 3, 2026, 4:31 p.m.