Triple
T17907267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KND |
E447733
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Curious Pictures
Curious Pictures was an American animation and production studio known for creating and producing a variety of television series, commercials, and animated content.
|
E1295167
|
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: Curious Pictures | Statement: [KND, productionCompany, Curious Pictures]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Curious Pictures Context triple: [KND, productionCompany, Curious Pictures]
-
A.
See Pictures
See Pictures is a film and television production company known for developing and producing narrative screen content.
-
B.
r/pics
r/pics is one of Reddit’s largest and most popular image-focused communities, where users share and discuss a wide variety of photographs and visual content.
-
C.
PicScout
PicScout is a technology company specializing in image recognition and copyright tracking solutions for the visual media industry.
-
D.
New Pictures
New Pictures is a British television production company known for creating high-profile drama series.
-
E.
Panoramic Pictures
Panoramic Pictures is a film production company known for backing independent and genre-focused movies such as "The Righteous."
- 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: Curious Pictures Triple: [KND, productionCompany, Curious Pictures]
Generated description
Curious Pictures was an American animation and production studio known for creating and producing a variety of television series, commercials, and animated content.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Curious Pictures Target entity description: Curious Pictures was an American animation and production studio known for creating and producing a variety of television series, commercials, and animated content.
-
A.
See Pictures
See Pictures is a film and television production company known for developing and producing narrative screen content.
-
B.
r/pics
r/pics is one of Reddit’s largest and most popular image-focused communities, where users share and discuss a wide variety of photographs and visual content.
-
C.
PicScout
PicScout is a technology company specializing in image recognition and copyright tracking solutions for the visual media industry.
-
D.
New Pictures
New Pictures is a British television production company known for creating high-profile drama series.
-
E.
Panoramic Pictures
Panoramic Pictures is a film production company known for backing independent and genre-focused movies such as "The Righteous."
- 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_69d8b9f6d394819082a6d69fd1e23d2f |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49e9d458881909e35e1c7a6e85436 |
completed | April 19, 2026, 9:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a031b2e0c5481908443e13839a0415d |
completed | May 12, 2026, 12:21 p.m. |
| NEDg | Description generation | batch_6a031bc800d081908493949af23dd360 |
completed | May 12, 2026, 12:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a031d4bf2dc8190b69eb8f8050b9955 |
completed | May 12, 2026, 12:30 p.m. |
Created at: April 10, 2026, 10:19 a.m.