Triple
T22527804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arab cinema |
E556953
|
entity |
| Predicate | hasNotableDirector |
P4744
|
FINISHED |
| Object |
Omar Amiralay
Omar Amiralay was a prominent Syrian documentary filmmaker known for his politically critical and socially engaged films that significantly influenced Arab cinema.
|
E1542482
|
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: Omar Amiralay | Statement: [Arab cinema, hasNotableDirector, Omar Amiralay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Omar Amiralay Context triple: [Arab cinema, hasNotableDirector, Omar Amiralay]
-
A.
Omar Abada
Omar Abada is a Tunisian professional basketball point guard known for his playmaking skills and leadership on the court.
-
B.
Omar Alfanno
Omar Alfanno is a Panamanian songwriter and composer renowned for crafting numerous Latin music hits across salsa and pop genres.
-
C.
Omar Samhan
Omar Samhan is an American former professional basketball center best known for starring at Saint Mary's College, where he became one of the program's most dominant and recognizable players.
-
D.
Omar Grand
Omar Grand is a music producer known for his work with the group American Teen.
-
E.
Omar Hassan-Reep
Omar Hassan-Reep is a film editor known for his work on the 2010 horror movie "Chain Letter."
- 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: Omar Amiralay Triple: [Arab cinema, hasNotableDirector, Omar Amiralay]
Generated description
Omar Amiralay was a prominent Syrian documentary filmmaker known for his politically critical and socially engaged films that significantly influenced Arab cinema.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Omar Amiralay Target entity description: Omar Amiralay was a prominent Syrian documentary filmmaker known for his politically critical and socially engaged films that significantly influenced Arab cinema.
-
A.
Omar Abada
Omar Abada is a Tunisian professional basketball point guard known for his playmaking skills and leadership on the court.
-
B.
Omar Alfanno
Omar Alfanno is a Panamanian songwriter and composer renowned for crafting numerous Latin music hits across salsa and pop genres.
-
C.
Omar Samhan
Omar Samhan is an American former professional basketball center best known for starring at Saint Mary's College, where he became one of the program's most dominant and recognizable players.
-
D.
Omar Grand
Omar Grand is a music producer known for his work with the group American Teen.
-
E.
Omar Hassan-Reep
Omar Hassan-Reep is a film editor known for his work on the 2010 horror movie "Chain Letter."
- 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_69e11e57483c8190b0887c4f8ff26446 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15ed411488190a51320930b9805c2 |
completed | April 29, 2026, 1:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b1dd894048190a50e4b3d24d26905 |
completed | May 18, 2026, 2:10 p.m. |
| NEDg | Description generation | batch_6a0b2560f8b08190aaba573e06d38d55 |
completed | May 18, 2026, 2:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b2729328481908faf3f88acfc9de1 |
completed | May 18, 2026, 2:50 p.m. |
Created at: April 16, 2026, 8:51 p.m.