Triple
T13918752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mongol Empire administration |
E334687
|
entity |
| Predicate | usedOffice |
P112289
|
FINISHED |
| Object | darughachi |
—
|
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: darughachi | Statement: [Mongol Empire administration, usedOffice, darughachi]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedOffice Context triple: [Mongol Empire administration, usedOffice, darughachi]
-
A.
usedOfficeFor
Indicates that an entity made use of a particular office or office space for some purpose or activity.
-
B.
usedByOffice
Indicates that something is utilized, operated, or employed by an office or office-related entity.
-
C.
officeIn
Indicates that one entity has an office located within the premises or jurisdiction of another entity.
-
D.
officeUnder
Indicates that one office is subordinate to, managed by, or organizationally within the authority of another office.
-
E.
worksWithOffice
Indicates that an entity collaborates or is professionally associated with a particular office or office-based organization.
- F. None of above. chosen
Provenance (4 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_69d81c5f739081908bc05b2461f54828 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de272753e48190bc609482635280ff |
completed | April 14, 2026, 11:38 a.m. |
| PD | Predicate disambiguation | batch_69de059e4ba881908554f72e889719fa |
completed | April 14, 2026, 9:15 a.m. |
| PDg | Predicate description generation | batch_69de239524688190a0f2408c239cfcaa |
completed | April 14, 2026, 11:23 a.m. |
Created at: April 9, 2026, 10:16 p.m.