Triple
T15367843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CHiPs (2017 film) |
E367462
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Panay Films
Panay Films is a film production company known for producing feature films such as the 2017 action-comedy "CHiPs."
|
E1153132
|
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: Panay Films | Statement: [CHiPs (2017 film), productionCompany, Panay Films]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Panay Films Context triple: [CHiPs (2017 film), productionCompany, Panay Films]
-
A.
Moro Films
Moro Films is a Spanish film production company known for backing contemporary Spanish-language cinema, including the drama "Felices 140."
-
B.
GMA Films
GMA Films is a Philippine film production and distribution company known for creating movies associated with the GMA Network’s television brands and stars.
-
C.
Aries Films
Aries Films is a film distribution company known for handling the release of independent and art-house movies such as "Bad Lieutenant."
-
D.
Morfina Films
Morfina Films is a film production company known for producing the Spanish drama film "Tristana."
-
E.
Miramar Films
Miramar Films is a film production company known for producing feature films such as the coming-of-age comedy "Adventureland."
- 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: Panay Films Triple: [CHiPs (2017 film), productionCompany, Panay Films]
Generated description
Panay Films is a film production company known for producing feature films such as the 2017 action-comedy "CHiPs."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Panay Films Target entity description: Panay Films is a film production company known for producing feature films such as the 2017 action-comedy "CHiPs."
-
A.
Moro Films
Moro Films is a Spanish film production company known for backing contemporary Spanish-language cinema, including the drama "Felices 140."
-
B.
GMA Films
GMA Films is a Philippine film production and distribution company known for creating movies associated with the GMA Network’s television brands and stars.
-
C.
Aries Films
Aries Films is a film distribution company known for handling the release of independent and art-house movies such as "Bad Lieutenant."
-
D.
Morfina Films
Morfina Films is a film production company known for producing the Spanish drama film "Tristana."
-
E.
Miramar Films
Miramar Films is a film production company known for producing feature films such as the coming-of-age comedy "Adventureland."
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e4a7cdc8190b7b48c97e774c306 |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff0b4e968c8190a16824ee3ede13b2 |
completed | May 9, 2026, 10:24 a.m. |
| NEDg | Description generation | batch_69ff0dc93af88190ae34fa3983aac820 |
completed | May 9, 2026, 10:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff0e467a148190871cb8a2dc660e06 |
completed | May 9, 2026, 10:36 a.m. |
Created at: April 10, 2026, 3:18 a.m.