Triple
T34976074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krakozhian |
E1008679
|
entity |
| Predicate | hasAssociatedFictionalConflict |
P103638
|
FINISHED |
| Object | civil conflict in 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: civil conflict in Krakozhia | Statement: [Krakozhian, hasAssociatedFictionalConflict, civil conflict in Krakozhia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAssociatedFictionalConflict Context triple: [Krakozhian, hasAssociatedFictionalConflict, civil conflict in Krakozhia]
-
A.
hasFictionalUniverseConflict
Indicates that there is a conflict or incompatibility between the fictional universes associated with the related entities.
-
B.
hasFictionalWar
chosen
Indicates that there exists a fictional or imagined war involving the related entities.
-
C.
fictionalConflict
Indicates a relationship where one fictional entity is in opposition, dispute, or struggle with another within a narrative context.
-
D.
contestedByFictionalCharacter
Indicates that a fictional character challenges, disputes, or opposes something, such as a claim, decision, or situation.
-
E.
hasPartOfConflict
Indicates that one conflict includes another conflict as a constituent or subordinate part of it.
- 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_69fd02680d948190a3463fb119ba8556 |
completed | May 7, 2026, 9:21 p.m. |
| PD | Predicate disambiguation | batch_69fcf89c69b4819082bbc564bd15137d |
completed | May 7, 2026, 8:39 p.m. |
Created at: May 3, 2026, 4:01 p.m.