Triple
T17374215
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mandip Gill |
E422392
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Mandip |
—
|
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: Mandip | Statement: [Mandip Gill, givenName, Mandip]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mandip Context triple: [Mandip Gill, givenName, Mandip]
-
A.
Mandip Gill
chosen
Mandip Gill is a British actress best known for playing companion Yasmin Khan in the long-running science fiction series Doctor Who.
-
B.
Parminder Nagra
Parminder Nagra is a British actress best known for her breakthrough lead role in the football-themed film "Bend It Like Beckham" and for her later work in television dramas such as "ER."
-
C.
Maheep Kapoor
Maheep Kapoor is an Indian jewelry designer and reality television personality known for appearing on the Netflix series "Fabulous Lives of Bollywood Wives."
-
D.
Nimrat Kaur
Nimrat Kaur is an Indian actress known for her acclaimed performances in films like "The Lunchbox" and "Airlift" as well as in international television series.
-
E.
Aparshakti Khurana
Aparshakti Khurana is an Indian actor, radio jockey, and television host known for his supporting roles in popular Bollywood films and his comic timing.
- 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_69d889d6535c81908be333c01deaec4e |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a6b71148190bb10e1fac400d6c3 |
completed | April 19, 2026, 2:14 a.m. |
Created at: April 10, 2026, 5:44 a.m.