Triple
T3428154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Skagen |
E72273
|
entity |
| Predicate | competesWith |
P1375
|
FINISHED |
| Object |
MVMT
MVMT is a contemporary watch and accessories brand known for its minimalist designs, direct-to-consumer online sales model, and affordable pricing aimed at younger consumers.
|
E356390
|
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: MVMT | Statement: [Skagen, competesWith, MVMT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MVMT Context triple: [Skagen, competesWith, MVMT]
-
A.
Vlone
Vlone is a track by Chicago rapper Valee known for its minimalist production and laid-back, off-kilter flow.
-
B.
Miu Miu
Miu Miu is an Italian high-fashion brand known for its playful, avant-garde take on womenswear and accessories.
-
C.
KITH
KITH is the ICAO airport code for Ithaca Tompkins International Airport in Ithaca, New York, which serves the Finger Lakes region with regional commercial flights.
-
D.
Kylie Cosmetics
Kylie Cosmetics is a makeup and beauty brand founded by Kylie Jenner, known for its trend-setting lip kits and social media–driven marketing.
-
E.
Vans
Vans is an American footwear and apparel brand best known for its skate shoes and strong ties to skateboarding and youth street culture.
- 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: MVMT Triple: [Skagen, competesWith, MVMT]
Generated description
MVMT is a contemporary watch and accessories brand known for its minimalist designs, direct-to-consumer online sales model, and affordable pricing aimed at younger consumers.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MVMT Target entity description: MVMT is a contemporary watch and accessories brand known for its minimalist designs, direct-to-consumer online sales model, and affordable pricing aimed at younger consumers.
-
A.
Vlone
Vlone is a track by Chicago rapper Valee known for its minimalist production and laid-back, off-kilter flow.
-
B.
Miu Miu
Miu Miu is an Italian high-fashion brand known for its playful, avant-garde take on womenswear and accessories.
-
C.
KITH
KITH is the ICAO airport code for Ithaca Tompkins International Airport in Ithaca, New York, which serves the Finger Lakes region with regional commercial flights.
-
D.
Kylie Cosmetics
Kylie Cosmetics is a makeup and beauty brand founded by Kylie Jenner, known for its trend-setting lip kits and social media–driven marketing.
-
E.
Vans
Vans is an American footwear and apparel brand best known for its skate shoes and strong ties to skateboarding and youth street culture.
- 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_69ad85ae14308190bcbc25cfa0246c0b |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb983f4608190abcc27aa7b926deb |
completed | March 8, 2026, 6:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b35478448481908e1c0f717d99f992 |
completed | March 13, 2026, 12:04 a.m. |
| NEDg | Description generation | batch_69b35546dfa0819081800009fbe8afe3 |
completed | March 13, 2026, 12:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b355cecc4c81908ecb5f83e89b4a00 |
completed | March 13, 2026, 12:09 a.m. |
Created at: March 8, 2026, 3:15 p.m.