Triple
T19363558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaman tehsil |
E484341
|
entity |
| Predicate | regionCode |
P208
|
FINISHED |
| Object | IN-RJ |
—
|
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: IN-RJ | Statement: [Kaman tehsil, regionCode, IN-RJ]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: IN-RJ Context triple: [Kaman tehsil, regionCode, IN-RJ]
-
A.
IN-RJ
chosen
IN-RJ is the ISO 3166-2 code representing the Indian state of Rajasthan.
-
B.
Rio Claro (RJ)
Rio Claro (RJ) is a small municipality in the state of Rio de Janeiro, Brazil, located in the Sul Fluminense region and known for its natural landscapes and rural character.
-
C.
Belford Roxo
Belford Roxo is a municipality in the state of Rio de Janeiro, Brazil, located in the Baixada Fluminense region of the Rio de Janeiro metropolitan area.
-
D.
Laranjeiras
Laranjeiras is a historic colonial-era city in the Brazilian state of Sergipe, known for its preserved architecture and rich Afro-Brazilian cultural traditions.
-
E.
Laranjeiras
Laranjeiras is a traditional residential neighborhood in Rio de Janeiro, Brazil, known for its historic architecture, tree-lined streets, and strong cultural and football heritage.
- 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_69d8e8d305088190ad13571532aa454c |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e619a897008190a2c62a50ca60de2d |
completed | April 20, 2026, 12:18 p.m. |
Created at: April 10, 2026, 1:34 p.m.