Triple
T3848492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pandey |
E85231
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Panday |
E85231
|
NE 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: Panday | Statement: [Pandey, hasVariant, Panday]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Panday Context triple: [Pandey, hasVariant, Panday]
-
A.
Bauta
Bauta is a municipality in western Cuba known for its proximity to Havana and its mix of rural communities and small urban centers.
-
B.
Andanin Vilas
Andanin Vilas is a daughter of Argentine tennis legend Guillermo Vilas.
-
C.
Guiuan
Guiuan is a coastal municipality in the province of Eastern Samar in the Philippines, known for its historic church and exposure to powerful typhoons such as Super Typhoon Haiyan.
-
D.
Pandey
chosen
Pandey is an Indian surname commonly associated with Brahmin communities, notably borne by figures such as the 19th-century revolutionary Mangal Pandey.
-
E.
Don Karlos
Don Karlos is a historical drama by Friedrich Schiller that explores political intrigue, personal freedom, and moral conflict in the court of 16th-century Spain.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69aed936de1c81908f91bed80f70abb2 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeebcc8a0481909c35161336bdfbf9 |
completed | March 9, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b50414acdc81909bf0b62afa3fe536 |
completed | March 14, 2026, 6:45 a.m. |
Created at: March 9, 2026, 3:19 p.m.