Triple
T15377660
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prospect |
E367710
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Shep Films
Shep Films is a film and television production company known for developing and producing narrative screen projects.
|
E1152733
|
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: Shep Films | Statement: [Prospect, productionCompany, Shep Films]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shep Films Context triple: [Prospect, productionCompany, Shep Films]
-
A.
Vinson Films
Vinson Films is a film production company known for producing the 2019 horror-comedy thriller "Ready or Not."
-
B.
Shoebox Films
Shoebox Films is a British film production company known for producing independent and auteur-driven movies, including the 2019 thriller "Serenity."
-
C.
Kestrel Films
Kestrel Films is a British film production company best known for producing Ken Loach’s acclaimed 1969 drama "Kes."
-
D.
Barwood Films
Barwood Films is a film production company co-founded by Barbra Streisand, known for producing several of her starring and directing projects.
-
E.
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."
- 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: Shep Films Triple: [Prospect, productionCompany, Shep Films]
Generated description
Shep Films is a film and television production company known for developing and producing narrative screen projects.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shep Films Target entity description: Shep Films is a film and television production company known for developing and producing narrative screen projects.
-
A.
Vinson Films
Vinson Films is a film production company known for producing the 2019 horror-comedy thriller "Ready or Not."
-
B.
Shoebox Films
Shoebox Films is a British film production company known for producing independent and auteur-driven movies, including the 2019 thriller "Serenity."
-
C.
Kestrel Films
Kestrel Films is a British film production company best known for producing Ken Loach’s acclaimed 1969 drama "Kes."
-
D.
Barwood Films
Barwood Films is a film production company co-founded by Barbra Streisand, known for producing several of her starring and directing projects.
-
E.
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."
- 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_69d85a1551a08190ba2caea7cd51c639 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e5ece1081908d7c1289258b9c1f |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff0b56dd1c81909a3933330e85fe0e |
completed | May 9, 2026, 10:24 a.m. |
| NEDg | Description generation | batch_69ff0c1fd2dc8190934b21837f0d8689 |
completed | May 9, 2026, 10:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff0c81636c81909536e69b48c5c400 |
completed | May 9, 2026, 10:29 a.m. |
Created at: April 10, 2026, 3:18 a.m.