Triple
T5929002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Patrik Frisk |
E131886
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Nautica |
E539228
|
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: Nautica | Statement: [Patrik Frisk, employer, Nautica]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nautica Context triple: [Patrik Frisk, employer, Nautica]
-
A.
Nautica
chosen
Nautica is an American lifestyle brand best known for its nautical-inspired apparel and accessories.
-
B.
Seabreeze
Seabreeze was a former neighboring city to Daytona Beach, Florida, that was eventually incorporated into the larger Daytona Beach municipality.
-
C.
Eclipse (yacht)
Eclipse is a luxury superyacht, once the world’s largest, renowned for its opulence, advanced security features, and association with Russian billionaire Roman Abramovich.
-
D.
Regal Unlimited
Regal Unlimited is a movie theater subscription service that lets members watch multiple films at Regal Cinemas for a flat monthly fee.
-
E.
Paparoa
Paparoa is a small rural settlement in New Zealand known for its historic village character and location in the Northland Region.
- 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_69c0085b75e88190a632f9691f9da48b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c038571d108190b4f3d242c068452f |
completed | March 22, 2026, 6:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0c059b08c8190aec3a8ee0119abed |
completed | March 23, 2026, 4:23 a.m. |
Created at: March 22, 2026, 4 p.m.