Triple
T11089271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Allah Quli Khan |
E262206
|
entity |
| Predicate | conflictTypeEngagedIn |
P61727
|
FINISHED |
| Object | inter-state warfare in Central Asia |
—
|
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: inter-state warfare in Central Asia | Statement: [Allah Quli Khan, conflictTypeEngagedIn, inter-state warfare in Central Asia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: conflictTypeEngagedIn Context triple: [Allah Quli Khan, conflictTypeEngagedIn, inter-state warfare in Central Asia]
-
A.
hasPartOfConflict
Indicates that one conflict includes another conflict as a constituent or subordinate part of it.
-
B.
conflictExperience
chosen
Indicates that an entity has undergone or been involved in a conflict, such as a dispute, struggle, or confrontation.
-
C.
militaryConflictIn
Indicates that a military conflict takes place within, or is geographically located in, a specified area or region.
-
D.
conflictType
Indicates the specific kind or category of conflict that characterizes the relationship or interaction between entities.
-
E.
conflictBelligerent
Indicates that an entity is a participating belligerent (e.g., a country, group, or force) in a specific conflict.
- 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_69d6aa9a40d88190a373e2c7e48285db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d799e844b08190987c7c8e8d626510 |
completed | April 9, 2026, 12:22 p.m. |
| PD | Predicate disambiguation | batch_69d744185a5881909ba4cf151d1798ec |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:27 p.m.