Triple
T19576109
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sardari Begum |
E489862
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Aseem Sinha |
—
|
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: Aseem Sinha | Statement: [Sardari Begum, editedBy, Aseem Sinha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aseem Sinha Context triple: [Sardari Begum, editedBy, Aseem Sinha]
-
A.
Aseem Sinha
chosen
Aseem Sinha is a film editor known for his work on the acclaimed Hindi film "Suraj Ka Satvan Ghoda."
-
B.
Aseem Kishore
Aseem Kishore is a technology writer and blogger known for creating practical guides and tutorials on software, web development, and digital tools.
-
C.
Aseem Hattangadi
Aseem Hattangadi is an Indian actor and the son of acclaimed actress Rohini Hattangadi.
-
D.
Sharan Narang
Sharan Narang is a machine learning researcher known for his work on large-scale natural language processing models, including contributions to the development of the T5 transformer architecture.
-
E.
Manju Sharma
Manju Sharma is known as the wife of Indian poet, politician, and public speaker Kumar Vishwas.
- 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_69d8e8dd9374819098e36349b3211663 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e64024c5b08190bbff6df633857874 |
completed | April 20, 2026, 3:03 p.m. |
Created at: April 10, 2026, 1:42 p.m.