Triple
T34976066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krakozhian |
E1008679
|
entity |
| Predicate | hasAssociatedFictionalCitizenship |
P96604
|
FINISHED |
| Object | Krakozhian passport |
—
|
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: Krakozhian passport | Statement: [Krakozhian, hasAssociatedFictionalCitizenship, Krakozhian passport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAssociatedFictionalCitizenship Context triple: [Krakozhian, hasAssociatedFictionalCitizenship, Krakozhian passport]
-
A.
fictionalCitizenship
chosen
Indicates that an entity is recognized as a citizen of a fictional or imaginary polity, realm, or jurisdiction.
-
B.
isCitizenOf
Indicates that a person holds legal nationality or citizenship status in a particular country or state.
-
C.
definedCitizenship
Indicates that a formal citizenship status has been legally established or specified for an entity.
-
D.
hasEponymCitizenship
Indicates that an entity’s eponym (the person or figure it is named after) holds or held a particular citizenship or national affiliation.
-
E.
hasHostCitizenship
Indicates that an entity holds citizenship in, or is a citizen of, a specified host country or jurisdiction.
- 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_69f76dc78a308190a1ac29ad4a9a4895 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fcf36d2894819089b7db8e91b63c9d |
completed | May 7, 2026, 8:17 p.m. |
| PD | Predicate disambiguation | batch_69fcf25c0a108190bfa823474098640b |
completed | May 7, 2026, 8:13 p.m. |
Created at: May 3, 2026, 4:01 p.m.