Triple
T16976837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NOR |
E411833
|
entity |
| Predicate | teamNickname |
P5076
|
FINISHED |
| Object | Admirals |
E389118
|
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: Admirals | Statement: [NOR, teamNickname, Admirals]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Admirals Context triple: [NOR, teamNickname, Admirals]
-
A.
Admirals
chosen
Admirals was a professional American football team based in Amsterdam that competed in NFL Europe.
-
B.
Almirante
Almirante is a coastal town in Panama known as a key port and transport hub in the Bocas del Toro region.
-
C.
Admiral Shane
Admiral Shane is a high-ranking U.S. Navy officer and key military leader in the science fiction action film "Battleship."
-
D.
Admiral
Admiral is a senior naval officer rank, typically the highest or among the highest in a navy, responsible for commanding large fleets and holding top-level strategic leadership roles.
-
E.
Admiral
Admiral is an Austrian sports betting and gaming company known for its prominent sponsorships in professional football and other sports.
- 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_69d886ca8f348190812768ea8d5055ce |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d183ff648190a3cb47242bf7e6ba |
completed | April 18, 2026, 6:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00d4755ffc8190a5c861462e33d526 |
completed | May 10, 2026, 6:54 p.m. |
Created at: April 10, 2026, 5:32 a.m.