Triple
T13735377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Digitata |
E329929
|
entity |
| Predicate | associatedAct |
P37
|
FINISHED |
| Object |
Lookbook
Lookbook is an American electronic pop duo known for its atmospheric synth-driven sound and emotive vocals.
|
E1057573
|
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: Lookbook | Statement: [Digitata, associatedAct, Lookbook]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lookbook Context triple: [Digitata, associatedAct, Lookbook]
-
A.
Best Look
Best Look is a style-focused award category at the MTV Europe Music Awards that recognizes the artist with the most notable fashion and visual presentation.
-
B.
HauteLook
HauteLook is an online flash-sale retailer specializing in limited-time discounts on fashion, beauty, and home goods.
-
C.
Long Look
Long Look is a village on the island of Tortola in the British Virgin Islands, known as one of the territory’s oldest free Black communities.
-
D.
Look magazine
Look magazine was a popular mid-20th-century American general-interest photojournalism magazine known for its extensive use of photography to cover news, culture, and social issues.
-
E.
Look
Look is a French cycling brand best known for pioneering clipless pedals and innovative carbon fiber bicycle frames used by professional racing teams.
- 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: Lookbook Triple: [Digitata, associatedAct, Lookbook]
Generated description
Lookbook is an American electronic pop duo known for its atmospheric synth-driven sound and emotive vocals.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lookbook Target entity description: Lookbook is an American electronic pop duo known for its atmospheric synth-driven sound and emotive vocals.
-
A.
Best Look
Best Look is a style-focused award category at the MTV Europe Music Awards that recognizes the artist with the most notable fashion and visual presentation.
-
B.
HauteLook
HauteLook is an online flash-sale retailer specializing in limited-time discounts on fashion, beauty, and home goods.
-
C.
Long Look
Long Look is a village on the island of Tortola in the British Virgin Islands, known as one of the territory’s oldest free Black communities.
-
D.
Look magazine
Look magazine was a popular mid-20th-century American general-interest photojournalism magazine known for its extensive use of photography to cover news, culture, and social issues.
-
E.
Look
Look is a French cycling brand best known for pioneering clipless pedals and innovative carbon fiber bicycle frames used by professional racing teams.
- 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_69d80772315881908f980cae40d91664 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69de020351fc8190a554a48c552e83b5 |
completed | April 14, 2026, 8:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d66cb088190be2621753d0a6740 |
completed | May 3, 2026, 7:09 p.m. |
| NEDg | Description generation | batch_69f79e7869648190ab0157bd0480b219 |
completed | May 3, 2026, 7:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f79f7216b08190800165d46172222c |
completed | May 3, 2026, 7:18 p.m. |
Created at: April 9, 2026, 9:55 p.m.