Triple
T9429899
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Washed Out |
E227344
|
entity |
| Predicate | associatedAct |
P37
|
FINISHED |
| Object |
Small Black
Small Black is an American indie band known for its hazy, synth-driven chillwave sound and lo-fi pop aesthetics.
|
E799646
|
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: Small Black | Statement: [Washed Out, associatedAct, Small Black]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Small Black Context triple: [Washed Out, associatedAct, Small Black]
-
A.
Mini
Mini is a young Bengali girl in Rabindranath Tagore’s short story "Kabuliwala," whose innocent friendship with an Afghan fruit seller forms the emotional core of the narrative.
-
B.
Mini
Mini is a British automotive marque best known for its compact, stylish small cars that originated with the iconic Mini of the 1960s.
-
C.
Kleiner
Kleiner is a surname most notably associated with Eugene Kleiner, a pioneering Silicon Valley venture capitalist and co-founder of the firm Kleiner Perkins.
-
D.
Thinline
Thinline is a slim, portable book format commonly used for Bibles that reduces bulk while retaining the full text.
-
E.
BL Mini 1000
The BL Mini 1000 is a small British economy car from the classic Mini range, produced under British Leyland and known for its compact size and distinctive styling.
- 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: Small Black Triple: [Washed Out, associatedAct, Small Black]
Generated description
Small Black is an American indie band known for its hazy, synth-driven chillwave sound and lo-fi pop aesthetics.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Small Black Target entity description: Small Black is an American indie band known for its hazy, synth-driven chillwave sound and lo-fi pop aesthetics.
-
A.
Mini
Mini is a young Bengali girl in Rabindranath Tagore’s short story "Kabuliwala," whose innocent friendship with an Afghan fruit seller forms the emotional core of the narrative.
-
B.
Mini
Mini is a British automotive marque best known for its compact, stylish small cars that originated with the iconic Mini of the 1960s.
-
C.
Kleiner
Kleiner is a surname most notably associated with Eugene Kleiner, a pioneering Silicon Valley venture capitalist and co-founder of the firm Kleiner Perkins.
-
D.
Thinline
Thinline is a slim, portable book format commonly used for Bibles that reduces bulk while retaining the full text.
-
E.
BL Mini 1000
The BL Mini 1000 is a small British economy car from the classic Mini range, produced under British Leyland and known for its compact size and distinctive styling.
- 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_69ca8436ba308190903e470776d2d893 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd7c94719c81909d7743a57c45e07f |
completed | April 1, 2026, 8:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d11038f7b88190bd6b895f5544c63e |
completed | April 4, 2026, 1:20 p.m. |
| NEDg | Description generation | batch_69d111a770c881909a2902d36cd7913c |
completed | April 4, 2026, 1:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d112634fb48190b4c7e9d997d27928 |
completed | April 4, 2026, 1:30 p.m. |
Created at: March 30, 2026, 7:49 p.m.