Triple
T22916697
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IMG |
E568749
|
entity |
| Predicate | hasDivision |
P35
|
FINISHED |
| Object | IMG Fashion |
—
|
NE NERFINISHED |
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: IMG Fashion | Statement: [IMG, hasDivision, IMG Fashion]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: IMG Fashion Context triple: [IMG, hasDivision, IMG Fashion]
-
A.
IMG Fashion
chosen
IMG Fashion is a division of global sports and talent management company IMG that specializes in producing and managing major fashion events, including New York Fashion Week and other international runway shows.
-
B.
fashion zone
Fashion Zone is a dedicated retail area within the Centro Santa Fe shopping mall that concentrates a variety of clothing, accessories, and style-focused stores.
-
C.
Fashn
Fashn is a city in Egypt located within the Beni Suef Governorate along the Nile River.
-
D.
fashion labs
Fashion labs are specialized facilities at Design School Kolding dedicated to experimentation, prototyping, and research in fashion design and related textile practices.
-
E.
Fashion (The Guardian)
Fashion (The Guardian) is The Guardian’s dedicated section covering style, clothing, trends, and the fashion industry with news, features, and commentary.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e2458d90c88190a58cead4e781ca6a |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1807a14648190b5d5f7d926f19320 |
completed | April 29, 2026, 3:52 a.m. |
Created at: April 17, 2026, 3:42 p.m.