Triple
T22858983
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vyjayanthimala |
E566858
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Nagin
Nagin is a 1954 Hindi fantasy thriller film, best known for its iconic snake-charmer music and for being one of Vyjayanthimala’s early breakthrough roles in Indian cinema.
|
E1558173
|
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: Nagin | Statement: [Vyjayanthimala, notableWork, Nagin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nagin Context triple: [Vyjayanthimala, notableWork, Nagin]
-
A.
Nagin
Nagin is a surname most notably associated with Ray Nagin, the former mayor of New Orleans.
-
B.
Naagin
Naagin is a popular Indian supernatural fantasy television series centered on shape-shifting serpent beings, produced by Ekta Kapoor under Balaji Telefilms.
-
C.
Tarpeena
Tarpeena is a small rural town and locality in South Australia, known historically for its timber and forestry industries.
-
D.
Rajkahini
Rajkahini is a Bengali period drama film set against the backdrop of the 1947 Partition of Bengal, known for its ensemble cast of women and its exploration of displacement, violence, and identity.
-
E.
Badal
Badal is a Barcelona Metro station that serves the area near Camp Nou stadium in Barcelona, Spain.
- 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: Nagin Triple: [Vyjayanthimala, notableWork, Nagin]
Generated description
Nagin is a 1954 Hindi fantasy thriller film, best known for its iconic snake-charmer music and for being one of Vyjayanthimala’s early breakthrough roles in Indian cinema.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nagin Target entity description: Nagin is a 1954 Hindi fantasy thriller film, best known for its iconic snake-charmer music and for being one of Vyjayanthimala’s early breakthrough roles in Indian cinema.
-
A.
Nagin
Nagin is a surname most notably associated with Ray Nagin, the former mayor of New Orleans.
-
B.
Naagin
Naagin is a popular Indian supernatural fantasy television series centered on shape-shifting serpent beings, produced by Ekta Kapoor under Balaji Telefilms.
-
C.
Tarpeena
Tarpeena is a small rural town and locality in South Australia, known historically for its timber and forestry industries.
-
D.
Rajkahini
Rajkahini is a Bengali period drama film set against the backdrop of the 1947 Partition of Bengal, known for its ensemble cast of women and its exploration of displacement, violence, and identity.
-
E.
Badal
Badal is a Barcelona Metro station that serves the area near Camp Nou stadium in Barcelona, Spain.
- 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_69e24589083081908d5694c4fdc80086 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17ebf1838819092b2b99205a2192f |
completed | April 29, 2026, 3:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0bae8ba1dc8190a2af113d88c210d0 |
completed | May 19, 2026, 12:27 a.m. |
| NEDg | Description generation | batch_6a0baf39c42c8190a6401ab150b80039 |
completed | May 19, 2026, 12:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0bafce9cf48190a826f713e095e3be |
completed | May 19, 2026, 12:33 a.m. |
Created at: April 17, 2026, 3:37 p.m.