Triple
T34976075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krakozhian |
E1008679
|
entity |
| Predicate | hasAssociatedFictionalCapital |
P150122
|
FINISHED |
| Object | capital city of Krakozhia |
—
|
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: capital city of Krakozhia | Statement: [Krakozhian, hasAssociatedFictionalCapital, capital city of Krakozhia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAssociatedFictionalCapital Context triple: [Krakozhian, hasAssociatedFictionalCapital, capital city of Krakozhia]
-
A.
fictionalCapital
chosen
Indicates that a location serves as the capital city within a fictional or imaginary political or geographic entity.
-
B.
hasFictionalCityContext
Indicates that something is associated with, set in, or contextualized by a fictional city.
-
C.
hasFictionalTownBasedOn
Indicates that a fictional town is modeled on, inspired by, or derived from a specific real-world town or location.
-
D.
hasFictionalCountySeatRole
Indicates that an entity serves in the role of county seat within a fictional or imaginary administrative setting.
-
E.
cityOfFictionalLocation
Indicates that a fictional location is situated within or associated with a particular city.
- 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_69fd05ba6b2c81909c62b46237d10365 |
completed | May 7, 2026, 9:35 p.m. |
| PD | Predicate disambiguation | batch_69fd03039e48819082b6e12c5453885a |
completed | May 7, 2026, 9:24 p.m. |
Created at: May 3, 2026, 4:01 p.m.