Triple
T7547113
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rolf Gerhardsen |
E178433
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Kristiania |
E3654
|
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: Kristiania | Statement: [Rolf Gerhardsen, placeOfBirth, Kristiania]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kristiania Context triple: [Rolf Gerhardsen, placeOfBirth, Kristiania]
-
A.
Oslo
chosen
Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
-
B.
Copenhagen
Copenhagen is the capital and largest city of Denmark, known for its historic architecture, vibrant cultural scene, and high quality of life.
-
C.
Copenhagen
Copenhagen is a popular American smokeless tobacco (chewing tobacco/dip) brand known for its long history and strong presence in the U.S. market.
-
D.
Bergens
The Bergens are a race of gloomy, troll-eating creatures who serve as the primary villains in the animated film "Trolls."
-
E.
Trondheim
Trondheim is a historic Norwegian city in Trøndelag county, known for its medieval Nidaros Cathedral and role as a former capital of Norway.
- 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_69c69f2cbe08819088f9eb0c03ef529b |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f89a7b2c8190b2ca57edbb4f0390 |
completed | March 27, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c856bb83b88190947c0efed84b891a |
completed | March 28, 2026, 10:31 p.m. |
Created at: March 27, 2026, 3:49 p.m.