Triple
T12226994
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maratha administration |
E291376
|
entity |
| Predicate | hasRevenueUnit |
P103887
|
FINISHED |
| Object | Mauza |
—
|
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: Mauza | Statement: [Maratha administration, hasRevenueUnit, Mauza]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRevenueUnit Context triple: [Maratha administration, hasRevenueUnit, Mauza]
-
A.
usesRevenueModel
Indicates that one entity applies or operates according to a particular revenue model to generate income.
-
B.
revenueLevel
Indicates the relative amount or tier of revenue associated with an entity or activity.
-
C.
hasBroadcastRevenueModel
Indicates that one entity uses or is associated with a particular revenue model based on broadcasting activities.
-
D.
revenue
Indicates the amount of income generated by an entity from its business activities or operations over a specified period.
-
E.
revenueSources
Indicates the relationship identifying where an entity’s revenue comes from or the different streams that generate its income.
- 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_69d6ab668acc8190963ba424049d6aee |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d924a3973c8190a882046963b320fb |
completed | April 10, 2026, 4:26 p.m. |
| PD | Predicate disambiguation | batch_69d91c41bcbc81909782f4e3c571b218 |
completed | April 10, 2026, 3:50 p.m. |
| PDg | Predicate description generation | batch_69d92468052c819090546f36d009a64f |
completed | April 10, 2026, 4:25 p.m. |
Created at: April 8, 2026, 9:51 p.m.