Triple
T19254873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Datia State |
E481488
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object | Datia |
—
|
NE NERFINISHED |
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: Datia | Statement: [Datia State, hasTown, Datia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Datia Context triple: [Datia State, hasTown, Datia]
-
A.
Datia
chosen
Datia is a historic town and administrative center in central India, known for its palaces and temples, located in the state of Madhya Pradesh.
-
B.
Diass
Diass is a commune in western Senegal that hosts the country’s main international gateway, Blaise Diagne International Airport.
-
C.
Tarusa
Tarusa is a small historic town in western Russia known for its scenic location on the Oka River and its associations with Russian artists and writers.
-
D.
Dahegam
Dahegam is a town in the Indian state of Gujarat known for its local commerce and role as a regional hub within the Gandhinagar area.
-
E.
Dausa
Dausa is a town and district headquarters in the Indian state of Rajasthan, known for its historical forts, stepwells, and proximity to Jaipur.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8cd9d1081908a181d02b88b59b8 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fb3459d08190a7c28ed3f8c82a97 |
completed | April 20, 2026, 10:08 a.m. |
Created at: April 10, 2026, 1:28 p.m.