Triple
T13847519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sverre of Norway |
E332845
|
entity |
| Predicate | deathPlace |
P21
|
FINISHED |
| Object | Bergen, Norway |
E74082
|
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: Bergen, Norway | Statement: [Sverre of Norway, deathPlace, Bergen, Norway]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bergen, Norway Context triple: [Sverre of Norway, deathPlace, Bergen, Norway]
-
A.
Bergen, Sweden
Bergen, Sweden is a small locality in Västra Götaland County known for its rural character and twinning relationship with Bergen, Germany.
-
B.
Bergen
Bergen is a city in western Germany, historically notable as the site of the 1759 Battle of Bergen during the Seven Years' War.
-
C.
Bergen
chosen
Bergen is Norway's second-largest city, renowned for its historic harbor, surrounding mountains and fjords, and role as a former Hanseatic trading hub.
-
D.
Bergens
The Bergens are a race of gloomy, troll-eating creatures who serve as the primary villains in the animated film "Trolls."
-
E.
Fornebu, Norway
Fornebu, Norway is a coastal area in Bærum just outside Oslo, known for its transformation from the city’s former main airport into a modern hub for technology companies, offices, and residential developments.
- 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_69d81c5ba13c8190839315f54768acfd |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02b2a9788190b164760adec64ef6 |
completed | April 14, 2026, 9:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd323be8d48190b0b289f25de06fb1 |
completed | May 8, 2026, 12:45 a.m. |
Created at: April 9, 2026, 10:14 p.m.