Triple
T14876597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sania Mirza |
E349883
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Sania |
E349883
|
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: Sania | Statement: [Sania Mirza, givenName, Sania]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sania Context triple: [Sania Mirza, givenName, Sania]
-
A.
Sania Mirza
chosen
Sania Mirza is a renowned Indian professional tennis player, widely regarded as one of the country’s greatest female athletes and a multiple Grand Slam doubles champion.
-
B.
Dheena
Dheena is a 2001 Tamil action film directed by AR Murugadoss that significantly boosted Ajith Kumar’s mass-hero image and popularized his nickname “Thala.”
-
C.
Mayar
Mayar is a mountain in the Grampian range of Angus, Scotland, popular with hikers and often climbed together with its neighboring peak Driesh.
-
D.
Djanira
Djanira was a prominent Brazilian modernist painter known for her vivid depictions of everyday life, religious themes, and popular culture.
-
E.
Anna Sabatini
Anna Sabatini was the mother of renowned Italian-English novelist Rafael Sabatini, likely part of the culturally rich background that influenced his literary career.
- 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_69d822ee4f408190b6ac3b2fa434f0df |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5e4e4448190a8796573bc6d1069 |
completed | April 15, 2026, 12:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe72aad76c8190b024651483d8f9ff |
completed | May 8, 2026, 11:32 p.m. |
Created at: April 10, 2026, 1:55 a.m.