Triple
T6455558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Funeral |
E141984
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Darkside
Darkside is a track by the American funeral doom metal band Funeral, known for its slow, heavy atmosphere and melancholic tone.
|
E593439
|
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: Darkside | Statement: [Funeral, hasPart, Darkside]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Darkside Context triple: [Funeral, hasPart, Darkside]
-
A.
Cold Dark Place
Cold Dark Place is an EP by the American metal band Mastodon that showcases a more melodic and atmospheric side of their sound.
-
B.
Dark Side of Night
Dark Side of Night is likely a track or segment from the work "Stop Drop and Roll!!!," contributing a darker or more atmospheric element to the overall project.
-
C.
Otherside
"Otherside" is a popular alternative rock song by the Red Hot Chili Peppers, known for its introspective lyrics and distinctive melodic guitar-driven sound.
-
D.
Otherside
"Otherside" is a track by Post Malone from his hit studio album "Beerbongs & Bentleys."
-
E.
From Darkness
From Darkness is a British crime drama television series featuring Anne-Marie Duff as a former police officer drawn back into an investigation that forces her to confront her traumatic past.
- 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: Darkside Triple: [Funeral, hasPart, Darkside]
Generated description
Darkside is a track by the American funeral doom metal band Funeral, known for its slow, heavy atmosphere and melancholic tone.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Darkside Target entity description: Darkside is a track by the American funeral doom metal band Funeral, known for its slow, heavy atmosphere and melancholic tone.
-
A.
Cold Dark Place
Cold Dark Place is an EP by the American metal band Mastodon that showcases a more melodic and atmospheric side of their sound.
-
B.
Dark Side of Night
Dark Side of Night is likely a track or segment from the work "Stop Drop and Roll!!!," contributing a darker or more atmospheric element to the overall project.
-
C.
Otherside
"Otherside" is a popular alternative rock song by the Red Hot Chili Peppers, known for its introspective lyrics and distinctive melodic guitar-driven sound.
-
D.
Otherside
"Otherside" is a track by Post Malone from his hit studio album "Beerbongs & Bentleys."
-
E.
From Darkness
From Darkness is a British crime drama television series featuring Anne-Marie Duff as a former police officer drawn back into an investigation that forces her to confront her traumatic past.
- 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_69c008d2f91c8190a8178767a35e08fc |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c069d4d588819090e8a56c46c0bfe9 |
completed | March 22, 2026, 10:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c64bdc4e808190a7c24b963ab0aa30 |
completed | March 27, 2026, 9:20 a.m. |
| NEDg | Description generation | batch_69c64ce18f6c8190910dcc2fc553328e |
completed | March 27, 2026, 9:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c64db784f08190b786c4de051ee527 |
completed | March 27, 2026, 9:28 a.m. |
Created at: March 22, 2026, 4:48 p.m.