Triple
T8605374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karlie Kloss |
E203782
|
entity |
| Predicate | hasModeledFor |
P17880
|
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: [Karlie Kloss, hasModeledFor, H&M]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: H&M Context triple: [Karlie Kloss, hasModeledFor, 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.
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.
Zara
Zara is the historical Italian name for the coastal Croatian city of Zadar on the Adriatic Sea.
-
D.
Zara
Zara is a character in the 1953 film noir "Pickup on South Street," involved in the story’s underworld of espionage and crime.
-
E.
Zara
Zara is a global fast-fashion retail brand known for rapidly translating runway trends into affordable clothing and accessories for a mass-market audience.
- 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_69ca832c23e4819095a9f3eea4a21828 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc46e9b6a881908f6a6c847519e5e5 |
completed | March 31, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea900cf708190abb550f592edbdf6 |
completed | April 2, 2026, 5:36 p.m. |
Created at: March 30, 2026, 6:24 p.m.