Triple
T14043249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scars on Broadway |
E337897
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object |
Sam Alayan
Sam Alayan is a musician best known as a member of the rock band Scars on Broadway.
|
E1075537
|
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: Sam Alayan | Statement: [Scars on Broadway, hasMember, Sam Alayan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sam Alayan Context triple: [Scars on Broadway, hasMember, Sam Alayan]
-
A.
Guy Nattiv
Guy Nattiv is an Israeli filmmaker and screenwriter known for his socially conscious dramas, including the Oscar-winning short film "Skin" and its feature-length adaptation.
-
B.
Michael Sela
Michael Sela was an Israeli immunologist renowned for his pioneering work on synthetic antigens and for helping develop the multiple sclerosis drug Copaxone.
-
C.
Joe Mimran
Joe Mimran is a Canadian fashion designer and entrepreneur best known for creating influential lifestyle brands such as Club Monaco and Joe Fresh.
-
D.
Paul Misraki
Paul Misraki was a French composer best known for his prolific film scores and popular songs in mid-20th-century European cinema.
-
E.
David Habib
David Habib is a French politician known for his centrist and pro-European positions and for helping to establish the Union of Democrats and Independents (UDI) party.
- 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: Sam Alayan Triple: [Scars on Broadway, hasMember, Sam Alayan]
Generated description
Sam Alayan is a musician best known as a member of the rock band Scars on Broadway.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sam Alayan Target entity description: Sam Alayan is a musician best known as a member of the rock band Scars on Broadway.
-
A.
Guy Nattiv
Guy Nattiv is an Israeli filmmaker and screenwriter known for his socially conscious dramas, including the Oscar-winning short film "Skin" and its feature-length adaptation.
-
B.
Michael Sela
Michael Sela was an Israeli immunologist renowned for his pioneering work on synthetic antigens and for helping develop the multiple sclerosis drug Copaxone.
-
C.
Joe Mimran
Joe Mimran is a Canadian fashion designer and entrepreneur best known for creating influential lifestyle brands such as Club Monaco and Joe Fresh.
-
D.
Paul Misraki
Paul Misraki was a French composer best known for his prolific film scores and popular songs in mid-20th-century European cinema.
-
E.
David Habib
David Habib is a French politician known for his centrist and pro-European positions and for helping to establish the Union of Democrats and Independents (UDI) party.
- 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_69d81c664e48819088cbd8f433aeffe5 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de312b94308190bd0961f5bc719c7b |
completed | April 14, 2026, 12:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbc33f79a88190999978d7f34632cd |
completed | May 6, 2026, 10:39 p.m. |
| NEDg | Description generation | batch_69fbc5b495fc81908ed80edb117e3844 |
completed | May 6, 2026, 10:50 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fbc6718b508190a06c9706a70e47a8 |
completed | May 6, 2026, 10:53 p.m. |
Created at: April 9, 2026, 10:20 p.m.