Triple
T38663936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mughal Subah of Agra |
E940403
|
entity |
| Predicate | hadRevenueSystem |
P112669
|
FINISHED |
| Object | zabt system |
—
|
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: zabt system | Statement: [Mughal Subah of Agra, hadRevenueSystem, zabt system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadRevenueSystem Context triple: [Mughal Subah of Agra, hadRevenueSystem, zabt system]
-
A.
hasRevenueSystem
chosen
Indicates that one entity possesses, uses, or is associated with a particular revenue-generating system or mechanism.
-
B.
hasRevenueUnit
Indicates that an entity’s revenue is measured, reported, or associated in terms of a specified unit (e.g., currency or measurement unit).
-
C.
hadSystem
Indicates that an entity possessed, used, or was associated with a particular system.
-
D.
hasGateRevenue
Indicates that an entity receives income from ticket sales or admissions (gate receipts) associated with another entity or event.
-
E.
usesRevenueModel
Indicates that one entity applies or operates according to a particular revenue model to generate income.
- 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.