Triple
T13853384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Øyer municipality |
E332999
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Lillehammer city |
E17762
|
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: Lillehammer city | Statement: [Øyer municipality, locatedNear, Lillehammer city]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lillehammer city Context triple: [Øyer municipality, locatedNear, Lillehammer city]
-
A.
Lillehammer
chosen
Lillehammer is a Norwegian town in the Gudbrandsdalen valley, best known internationally for staging the 1994 Winter Olympics.
-
B.
Lillehammer municipality
Lillehammer municipality is a local government area in Innlandet county, Norway, best known for the town of Lillehammer, which hosted the 1994 Winter Olympics.
-
C.
Trondheim
Trondheim is a historic Norwegian city in Trøndelag county, known for its medieval Nidaros Cathedral and role as a former capital of Norway.
-
D.
Lørenskog
Lørenskog is a suburban municipality in Viken county, Norway, located just east of Oslo and known for its residential areas and commercial centers.
-
E.
Lysaker
Lysaker is a key transport and business hub in the western part of the Oslo metropolitan area in Norway, featuring a major railway and commuter center.
- 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_69de02da9460819093a3ec5a3c62ea81 |
completed | April 14, 2026, 9:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7ce6c0c3c8190911b56b20c9eb955 |
completed | May 3, 2026, 10:38 p.m. |
Created at: April 9, 2026, 10:14 p.m.