Triple
T19838064
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mau |
E476650
|
entity |
| Predicate | district |
P2709
|
FINISHED |
| Object | Mau district |
—
|
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: Mau district | Statement: [Mau, district, Mau district]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mau district Context triple: [Mau, district, Mau district]
-
A.
Mau district
chosen
Mau district is an administrative region in the Indian state of Uttar Pradesh, known for its textile industry and location in the eastern Purvanchal area.
-
B.
Mala District
Mala District is an administrative district in Peru’s Cañete Province, known for its agricultural production and coastal valley landscape.
-
C.
Marangani District
Marangani District is an administrative district located in the high Andean region of southern Peru, within the Cusco Department.
-
D.
Bunda District
Bunda District is an administrative district in northern Tanzania, located within the Mara Region near the eastern shores of Lake Victoria.
-
E.
Susut District
Susut District is an administrative district in Bali, Indonesia, located within Bangli Regency and known for its rural villages and traditional Balinese culture.
- 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_69d8e51d39d081909bcfafeaaf3d2fcc |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65803bb988190a4e7c0035058feba |
completed | April 20, 2026, 4:44 p.m. |
Created at: April 10, 2026, 1:50 p.m.