Triple
T22926761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khatun |
E569330
|
entity |
| Predicate | roleRelation |
P84787
|
FINISHED |
| Object | female counterpart of khan |
—
|
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: female counterpart of khan | Statement: [Khatun, roleRelation, female counterpart of khan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleRelation Context triple: [Khatun, roleRelation, female counterpart of khan]
-
A.
termRelationTo
Indicates a general relational association between one term and another, without specifying the exact nature of that relationship.
-
B.
subjectRelation
chosen
Indicates that one entity stands in a specified relational role or connection to another entity.
-
C.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
D.
identityRelation
Indicates that two entities are in fact the very same entity, not merely similar or equivalent.
-
E.
titleRelation
Indicates a relationship where one entity serves as the title, designation, or formal name associated with another entity.
- 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_69e2458f7d008190901dccbaebeaba24 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f180da10648190a67ba113fa8c1370 |
completed | April 29, 2026, 3:54 a.m. |
| PD | Predicate disambiguation | batch_69ef3b7c5fc081909ac50c5c8569cc19 |
completed | April 27, 2026, 10:33 a.m. |
Created at: April 17, 2026, 3:43 p.m.