Triple
T20237616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mamta |
E498191
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object |
Kamaldeep
Kamaldeep is a cinematographer known for working on the film "Mamta."
|
E1421441
|
NE FINISHED |
How this triple was built (4 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: Kamaldeep | Statement: [Mamta, cinematographyBy, Kamaldeep]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kamaldeep Context triple: [Mamta, cinematographyBy, Kamaldeep]
-
A.
Amandeep Singh
Amandeep Singh is an actor who appeared in the 2018 biographical thriller film "Hotel Mumbai."
-
B.
Sandeep Singh
Sandeep Singh is a former Indian field hockey defender renowned as one of the world’s best drag-flickers and a key figure in India’s modern hockey success.
-
C.
Amarjeet
Amarjeet is a Canadian politician best known as Amarjeet Sohi, who has served as mayor of Edmonton and as a federal cabinet minister.
-
D.
Agam Darshi
Agam Darshi is a British-Canadian actress and filmmaker best known for her genre television work, including a prominent role on the sci-fi series "Sanctuary."
-
E.
Kanwal
Kanwal is a residential suburb on the Central Coast of New South Wales, Australia, known for its family-friendly community and proximity to local schools, parks, and medical facilities.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Kamaldeep Triple: [Mamta, cinematographyBy, Kamaldeep]
Generated description
Kamaldeep is a cinematographer known for working on the film "Mamta."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kamaldeep Target entity description: Kamaldeep is a cinematographer known for working on the film "Mamta."
-
A.
Amandeep Singh
Amandeep Singh is an actor who appeared in the 2018 biographical thriller film "Hotel Mumbai."
-
B.
Sandeep Singh
Sandeep Singh is a former Indian field hockey defender renowned as one of the world’s best drag-flickers and a key figure in India’s modern hockey success.
-
C.
Amarjeet
Amarjeet is a Canadian politician best known as Amarjeet Sohi, who has served as mayor of Edmonton and as a federal cabinet minister.
-
D.
Agam Darshi
Agam Darshi is a British-Canadian actress and filmmaker best known for her genre television work, including a prominent role on the sci-fi series "Sanctuary."
-
E.
Kanwal
Kanwal is a residential suburb on the Central Coast of New South Wales, Australia, known for its family-friendly community and proximity to local schools, parks, and medical facilities.
- F. None of above. chosen
Provenance (5 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_69da6274c58c81909c646eabed6f4f30 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6716b4c148190bf663b8a747fbfa5 |
completed | April 20, 2026, 6:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a085a15b7848190b55a1e8b52503690 |
completed | May 16, 2026, 11:50 a.m. |
| NEDg | Description generation | batch_6a085a6a94e88190a3f2f2cf89013d44 |
completed | May 16, 2026, 11:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a085aca03748190b3d0544ebda3b8a7 |
completed | May 16, 2026, 11:53 a.m. |
Created at: April 11, 2026, 11:40 p.m.