Triple
T9846308
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hiroshima mon amour |
E239349
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Como-Films
Como-Films is a French film production company best known for producing Alain Resnais’s influential 1959 film "Hiroshima mon amour."
|
E824843
|
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: Como-Films | Statement: [Hiroshima mon amour, productionCompany, Como-Films]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Como-Films Context triple: [Hiroshima mon amour, productionCompany, Como-Films]
-
A.
CNN Films
CNN Films is a documentary film division of CNN that produces and acquires non-fiction feature films for theatrical release and television broadcast.
-
B.
Beyond Films
Beyond Films is an Australian film distribution and production company known for handling a range of independent and international titles.
-
C.
AC Films
AC Films is a film production company known for working on the documentary "Human Flow," which explores the global refugee crisis.
-
D.
Sketch Films
Sketch Films is a television production company best known for its work on the supernatural drama series "Sleepy Hollow."
-
E.
Adopt Films
Adopt Films is an independent film distribution company known for releasing art-house and international cinema in the United States.
- 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: Como-Films Triple: [Hiroshima mon amour, productionCompany, Como-Films]
Generated description
Como-Films is a French film production company best known for producing Alain Resnais’s influential 1959 film "Hiroshima mon amour."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Como-Films Target entity description: Como-Films is a French film production company best known for producing Alain Resnais’s influential 1959 film "Hiroshima mon amour."
-
A.
CNN Films
CNN Films is a documentary film division of CNN that produces and acquires non-fiction feature films for theatrical release and television broadcast.
-
B.
Beyond Films
Beyond Films is an Australian film distribution and production company known for handling a range of independent and international titles.
-
C.
AC Films
AC Films is a film production company known for working on the documentary "Human Flow," which explores the global refugee crisis.
-
D.
Sketch Films
Sketch Films is a television production company best known for its work on the supernatural drama series "Sleepy Hollow."
-
E.
Adopt Films
Adopt Films is an independent film distribution company known for releasing art-house and international cinema in the United States.
- 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_69ca84e3f0c48190ada72a65ebd50efd |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb36156308190b26892702f3b41e0 |
completed | April 2, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1d5e1b67c8190ad7b57ea423511d8 |
completed | April 5, 2026, 3:24 a.m. |
| NEDg | Description generation | batch_69d1d6a385ac8190b5dd11adfbb7578d |
completed | April 5, 2026, 3:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1d75210f4819096ee05a8b870581e |
completed | April 5, 2026, 3:30 a.m. |
Created at: March 30, 2026, 8:34 p.m.