Triple
T14043341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scars on Broadway |
E337897
|
entity |
| Predicate | associatedAct |
P37
|
FINISHED |
| Object |
Dope
Dope is an American industrial metal band known for its aggressive sound, dark aesthetic, and covers of popular songs.
|
E1075553
|
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: Dope | Statement: [Scars on Broadway, associatedAct, Dope]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dope Context triple: [Scars on Broadway, associatedAct, Dope]
-
A.
Dope
"Dope" is a hip-hop single by rapper Legend, showcasing his style and lyrical approach within the genre.
-
B.
Dope
Dope is a 2015 coming-of-age comedy-drama film that follows a geeky teenager navigating life in a tough Los Angeles neighborhood after a chance encounter with the underground drug world.
-
C.
Dope
"Dope" is a song by Canadian singer-songwriter Jessie Reyez that showcases her raw, emotional vocal style and confessional lyricism.
-
D.
Dope Money
Dope Money is a track from Nigerian rapper Olamide’s album "Baddest Guy Ever Liveth," showcasing his street-influenced hip-hop style and lyrical bravado.
-
E.
The Magnificent Dope
The Magnificent Dope is a 1942 American comedy film starring Henry Fonda and Lynn Bari that satirizes self-improvement fads and the pursuit of success.
- 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: Dope Triple: [Scars on Broadway, associatedAct, Dope]
Generated description
Dope is an American industrial metal band known for its aggressive sound, dark aesthetic, and covers of popular songs.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dope Target entity description: Dope is an American industrial metal band known for its aggressive sound, dark aesthetic, and covers of popular songs.
-
A.
Dope
"Dope" is a hip-hop single by rapper Legend, showcasing his style and lyrical approach within the genre.
-
B.
Dope
Dope is a 2015 coming-of-age comedy-drama film that follows a geeky teenager navigating life in a tough Los Angeles neighborhood after a chance encounter with the underground drug world.
-
C.
Dope
"Dope" is a song by Canadian singer-songwriter Jessie Reyez that showcases her raw, emotional vocal style and confessional lyricism.
-
D.
Dope Money
Dope Money is a track from Nigerian rapper Olamide’s album "Baddest Guy Ever Liveth," showcasing his street-influenced hip-hop style and lyrical bravado.
-
E.
The Magnificent Dope
The Magnificent Dope is a 1942 American comedy film starring Henry Fonda and Lynn Bari that satirizes self-improvement fads and the pursuit of success.
- 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.