Triple
T15553800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hailey Bieber |
E370817
|
entity |
| Predicate | hasModeledFor |
P17880
|
FINISHED |
| Object | Topshop |
E1137799
|
NE FINISHED |
How this triple was built (2 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: Topshop | Statement: [Hailey Bieber, hasModeledFor, Topshop]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Topshop Context triple: [Hailey Bieber, hasModeledFor, Topshop]
-
A.
Topshop
chosen
Topshop is a British fast-fashion retailer known for its trendy, youth-oriented clothing and accessories.
-
B.
Topshop Unique
Topshop Unique was the premium, runway-focused line of the British high-street fashion retailer Topshop, known for showing at London Fashion Week.
-
C.
Primark
Primark is a major Irish-founded fast-fashion retail chain known for its low-priced clothing, accessories, and home goods, operating under the Penneys brand in Ireland and Primark elsewhere in Europe and the United States.
-
D.
H&M
H&M, in this context, refers to the historic Hudson and Manhattan Railroad, an early 20th-century rapid transit system that connected Manhattan with New Jersey and served as a predecessor to today’s PATH trains.
-
E.
H&M
H&M is a global fast-fashion retail chain known for offering trendy clothing and accessories at affordable prices.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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. |
Created at: April 10, 2026, 4:09 a.m.