Triple
T15553812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hailey Bieber |
E370817
|
entity |
| Predicate | hasModeledFor |
P17880
|
FINISHED |
| Object |
Guess Jeans
Guess Jeans is an American fashion brand best known for its trend-driven denim and casual apparel.
|
E1163894
|
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: Guess Jeans | Statement: [Hailey Bieber, hasModeledFor, Guess Jeans]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guess Jeans Context triple: [Hailey Bieber, hasModeledFor, Guess Jeans]
-
A.
Hudson Jeans
Hudson Jeans is a premium American denim brand known for its high-quality, fashion-forward jeans and strong presence in the contemporary designer market.
-
B.
Pepe Jeans
Pepe Jeans is a British denim and casualwear fashion brand known for its trendy jeans and youthful, urban style.
-
C.
Sasson Jeans
Sasson Jeans was a popular American denim and sportswear brand that gained prominence in the late 1970s and 1980s for its fashion-forward jeans and memorable advertising campaigns.
-
D.
Isabel Jeans
Isabel Jeans was a British stage and film actress known for her sophisticated roles in early 20th-century cinema, including appearances in several Alfred Hitchcock films.
-
E.
Desmond Jeans
Desmond Jeans was a British actor active in the early to mid-20th century, known for his work on stage and in film.
- 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: Guess Jeans Triple: [Hailey Bieber, hasModeledFor, Guess Jeans]
Generated description
Guess Jeans is an American fashion brand best known for its trend-driven denim and casual apparel.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Guess Jeans Target entity description: Guess Jeans is an American fashion brand best known for its trend-driven denim and casual apparel.
-
A.
Hudson Jeans
Hudson Jeans is a premium American denim brand known for its high-quality, fashion-forward jeans and strong presence in the contemporary designer market.
-
B.
Pepe Jeans
Pepe Jeans is a British denim and casualwear fashion brand known for its trendy jeans and youthful, urban style.
-
C.
Sasson Jeans
Sasson Jeans was a popular American denim and sportswear brand that gained prominence in the late 1970s and 1980s for its fashion-forward jeans and memorable advertising campaigns.
-
D.
Isabel Jeans
Isabel Jeans was a British stage and film actress known for her sophisticated roles in early 20th-century cinema, including appearances in several Alfred Hitchcock films.
-
E.
Desmond Jeans
Desmond Jeans was a British actor active in the early to mid-20th century, known for his work on stage and in film.
- 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_69d85cc6cf40819091f4a5facee1ebe6 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04a96c0c88190808f68601a36b506 |
completed | April 16, 2026, 2:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff456209288190aba6debd434af741 |
completed | May 9, 2026, 2:32 p.m. |
| NEDg | Description generation | batch_69ff471cb68c8190924e894b190f15f4 |
completed | May 9, 2026, 2:39 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff47aeddac8190a87024019ecb1396 |
completed | May 9, 2026, 2:41 p.m. |
Created at: April 10, 2026, 4:09 a.m.