Triple

T2899247
Position Surface form Disambiguated ID Type / Status
Subject Jon Kortajarena E62615 entity
Predicate hasWorkedAsFaceOfBrand P29719 FINISHED
Object H&M E233546 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: H&M | Statement: [Jon Kortajarena, hasWorkedAsFaceOfBrand, H&M]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: H&M
Context triple: [Jon Kortajarena, hasWorkedAsFaceOfBrand, H&M]
  • A. H&M chosen
    H&M is a global fast-fashion retail chain known for offering trendy clothing and accessories at affordable prices.
  • B. Zara
    Zara is the historical Italian name for the coastal Croatian city of Zadar on the Adriatic Sea.
  • C. Uniqlo
    Uniqlo is a global Japanese clothing retailer known for its affordable, minimalist casual wear and functional basics.
  • D. 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.
  • E. Marshalls
    Marshalls is a major American off-price department store chain known for selling brand-name clothing, home goods, and accessories at discounted 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_69ab4c3e070c8190b78d3d2c005876dd completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abe0ad7bbc8190822738baa6935b74 completed March 7, 2026, 8:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69b0318c544881909f6aabfb2d25e724 completed March 10, 2026, 2:58 p.m.
Created at: March 6, 2026, 10:10 p.m.