Triple
T38663935
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mughal Subah of Agra |
E940403
|
entity |
| Predicate | hadJudicialOfficialTitle |
P32953
|
FINISHED |
| Object | qazi |
—
|
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: qazi | Statement: [Mughal Subah of Agra, hadJudicialOfficialTitle, qazi]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadJudicialOfficialTitle Context triple: [Mughal Subah of Agra, hadJudicialOfficialTitle, qazi]
-
A.
hasJudicialRank
Indicates that an entity holds a specified level or position within a judicial or court hierarchy.
-
B.
hasJudicialOfficer
Indicates that an entity is associated with or served by a specific judicial officer (such as a judge or magistrate) responsible for legal or court-related functions.
-
C.
notableOfficerTitle
Indicates that an entity holds or is associated with a particularly distinguished or noteworthy officer position or title.
-
D.
hadJudicialFunction
chosen
Indicates that an entity exercised or was assigned an official judicial role, authority, or responsibility in relation to another entity or context.
-
E.
heldJudicialPositionIn
Indicates that an entity served in an official judicial role or office within a specified jurisdiction or court.
- 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_69f76edfde348190bf6529d9f49ecd62 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcdfbc71c481908ba7f87907b17782 |
completed | May 7, 2026, 6:53 p.m. |
| PD | Predicate disambiguation | batch_69fcdbe580b8819087f143596b2c79c0 |
completed | May 7, 2026, 6:37 p.m. |
Created at: May 3, 2026, 4:33 p.m.