Triple
T6960484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mads Mikkelsen |
E161354
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Polar |
E572664
|
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: Polar | Statement: [Mads Mikkelsen, notableWork, Polar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Polar Context triple: [Mads Mikkelsen, notableWork, Polar]
-
A.
Polar
chosen
Polar is a 2019 action-thriller film directed by Jonas Åkerlund, based on the Dark Horse graphic novel about an aging assassin forced out of retirement.
-
B.
Polar Frontier
Polar Frontier is an Arctic-themed exhibit at the Columbus Zoo and Aquarium that showcases polar bears and other cold-climate wildlife in naturalistic habitats.
-
C.
Polyarny
Polyarny is a Russian naval town in Murmansk Oblast known as a key base for the Northern Fleet and submarine operations in the Arctic region.
-
D.
Polaria
Polaria is an Arctic-themed experience center and aquarium in Tromsø, Norway, focusing on polar research, climate, and marine life.
-
E.
Campo de Hielo Norte
Campo de Hielo Norte is a vast Patagonian ice field in southern Chile, known as one of the largest mid-latitude ice masses in the world and a major source of outlet glaciers and freshwater.
- 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_69c68852a9a0819097797e31d492e273 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6daeee4d48190b078beeebb0053f4 |
completed | March 27, 2026, 7:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7589892888190bf240bdbf107efcc |
completed | March 28, 2026, 4:27 a.m. |
Created at: March 27, 2026, 2:29 p.m.