Triple
T21428268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saagar |
E528617
|
entity |
| Predicate | distributor |
P1951
|
FINISHED |
| Object |
Sippy Films
Sippy Films is an Indian film production and distribution company best known for backing several classic and influential Bollywood movies.
|
E1484106
|
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: Sippy Films | Statement: [Saagar, distributor, Sippy Films]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sippy Films Context triple: [Saagar, distributor, Sippy Films]
-
A.
See-Saw Films
See-Saw Films is a British-Australian film and television production company known for acclaimed works such as the Academy Award–winning drama "The King’s Speech."
-
B.
Sister Pictures
Sister Pictures is a British television production company known for creating high-profile, critically acclaimed drama series.
-
C.
Sketch Films
Sketch Films is a television production company best known for its work on the supernatural drama series "Sleepy Hollow."
-
D.
Tapioca Films
Tapioca Films is a French film production company known for working on visually inventive and offbeat projects such as Jean-Pierre Jeunet’s "Micmacs à tire-larigot."
-
E.
Cineyug Films
Cineyug Films is an Indian film production company known for backing major Bollywood projects and entertainment ventures.
- 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: Sippy Films Triple: [Saagar, distributor, Sippy Films]
Generated description
Sippy Films is an Indian film production and distribution company best known for backing several classic and influential Bollywood movies.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sippy Films Target entity description: Sippy Films is an Indian film production and distribution company best known for backing several classic and influential Bollywood movies.
-
A.
See-Saw Films
See-Saw Films is a British-Australian film and television production company known for acclaimed works such as the Academy Award–winning drama "The King’s Speech."
-
B.
Sister Pictures
Sister Pictures is a British television production company known for creating high-profile, critically acclaimed drama series.
-
C.
Sketch Films
Sketch Films is a television production company best known for its work on the supernatural drama series "Sleepy Hollow."
-
D.
Tapioca Films
Tapioca Films is a French film production company known for working on visually inventive and offbeat projects such as Jean-Pierre Jeunet’s "Micmacs à tire-larigot."
-
E.
Cineyug Films
Cineyug Films is an Indian film production company known for backing major Bollywood projects and entertainment ventures.
- 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_69e0c455f3688190810bc96365791b0f |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b3e74bcc81909ad66e3c59152ffc |
completed | April 22, 2026, 11:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09c2aa56b081909ddfc07b7b5fd60e |
completed | May 17, 2026, 1:29 p.m. |
| NEDg | Description generation | batch_6a09c3a036408190a759223303268183 |
completed | May 17, 2026, 1:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09c49aa5b881908ac2e920684a9fa2 |
completed | May 17, 2026, 1:37 p.m. |
Created at: April 16, 2026, 5:49 p.m.