Triple
T9659273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | H&M |
E233546
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | Hennes & Mauritz AB |
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: Hennes & Mauritz AB | Statement: [H&M, fullName, Hennes & Mauritz AB]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hennes & Mauritz AB Context triple: [H&M, fullName, Hennes & Mauritz AB]
-
A.
H&M
chosen
H&M is a global fast-fashion retail chain known for offering trendy clothing and accessories at affordable prices.
-
B.
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.
-
C.
Benetton Group
Benetton Group is a global Italian fashion brand and retailer known for its colorful knitwear, casual clothing, and provocative social-issue advertising campaigns.
-
D.
Kering
Kering is a French multinational luxury group that owns and manages high-end fashion and leather goods brands such as Gucci, Saint Laurent, and Bottega Veneta.
-
E.
Hugo Boss
Hugo Boss is a German luxury fashion house known for its high-end menswear, fragrances, and accessories.
- 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_69ca848c1ba88190b84b410cd14627fc |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9bdfc3b08190835e86ff99663214 |
completed | April 1, 2026, 10:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d18a0b84a0819083191beeaf8d968b |
completed | April 4, 2026, 10 p.m. |
Created at: March 30, 2026, 8:14 p.m.