Triple
T9659283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | H&M |
E233546
|
entity |
| Predicate | originalName |
P65
|
FINISHED |
| Object | Hennes |
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 | Statement: [H&M, originalName, Hennes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hennes Context triple: [H&M, originalName, Hennes]
-
A.
Esprit
Esprit is an international fashion brand known for its casual, contemporary clothing and lifestyle products.
-
B.
Ursula Jeans
Ursula Jeans was a British stage and film actress known for her versatile character roles in mid-20th-century cinema and theatre.
-
C.
Hilfiger Denim
Hilfiger Denim is a casual clothing line under the Tommy Hilfiger fashion label, focusing on youthful, denim-centered apparel with a classic American style.
-
D.
H&M
chosen
H&M is a global fast-fashion retail chain known for offering trendy clothing and accessories at affordable prices.
-
E.
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.
- 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.