Triple
T14637730
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | At Any Price |
E343648
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Noruz Films
Noruz Films is an independent film production company known for producing character-driven dramas such as the feature film "At Any Price."
|
E1110002
|
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: Noruz Films | Statement: [At Any Price, productionCompany, Noruz Films]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Noruz Films Context triple: [At Any Price, productionCompany, Noruz Films]
-
A.
Artina Films
Artina Films is a film production company known for backing notable independent and auteur-driven movies, including Tom Ford’s psychological thriller "Nocturnal Animals."
-
B.
Mecca Films
Mecca Films is a film production company best known for producing the 2004 hip-hop dance drama movie "You Got Served."
-
C.
Levantine Films
Levantine Films is an independent film production company known for backing acclaimed, socially conscious movies such as "Hidden Figures."
-
D.
Lenfilm
Lenfilm is one of Russia’s oldest and most prominent film studios, based in Saint Petersburg and known for producing many classic Soviet-era movies.
-
E.
Swaka Films
Swaka Films is a film production company known for producing the 2013 adaptation of "Romeo & Juliet."
- 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: Noruz Films Triple: [At Any Price, productionCompany, Noruz Films]
Generated description
Noruz Films is an independent film production company known for producing character-driven dramas such as the feature film "At Any Price."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Noruz Films Target entity description: Noruz Films is an independent film production company known for producing character-driven dramas such as the feature film "At Any Price."
-
A.
Artina Films
Artina Films is a film production company known for backing notable independent and auteur-driven movies, including Tom Ford’s psychological thriller "Nocturnal Animals."
-
B.
Mecca Films
Mecca Films is a film production company best known for producing the 2004 hip-hop dance drama movie "You Got Served."
-
C.
Levantine Films
Levantine Films is an independent film production company known for backing acclaimed, socially conscious movies such as "Hidden Figures."
-
D.
Lenfilm
Lenfilm is one of Russia’s oldest and most prominent film studios, based in Saint Petersburg and known for producing many classic Soviet-era movies.
-
E.
Swaka Films
Swaka Films is a film production company known for producing the 2013 adaptation of "Romeo & Juliet."
- 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_69d822dffc3c8190aa173b90761bffda |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb4aca6448190adf1042dfbfef716 |
completed | April 14, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fda934ec3c81909eb3c3a54260436b |
completed | May 8, 2026, 9:13 a.m. |
| NEDg | Description generation | batch_69fdb1ad32a4819088e5831f3d74ea4e |
completed | May 8, 2026, 9:49 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fdb316479c81909343196bb89e5e57 |
completed | May 8, 2026, 9:55 a.m. |
Created at: April 10, 2026, 1:26 a.m.