Triple
T15333682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hordaland |
E366605
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Odda |
E365827
|
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: Odda | Statement: [Hordaland, containsCity, Odda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Odda Context triple: [Hordaland, containsCity, Odda]
-
A.
Odda
chosen
Odda is a town in western Norway known for its dramatic fjord landscape, industrial heritage, and proximity to popular hiking destinations like Trolltunga.
-
B.
Odensala
Odensala is a locality within Östersund Municipality in Jämtland County, Sweden, functioning as a residential area near the city of Östersund.
-
C.
Harestua
Harestua is a village in Viken county, Norway, known for its residential community and proximity to the Harestua Solar Observatory.
-
D.
Oederan
Oederan is a small historic town in the Free State of Saxony in eastern Germany, known for its traditional architecture and model railway park.
-
E.
Mora
Mora is a canton in Costa Rica’s San José Province known for its rural landscapes, agricultural activities, and small-town communities.
- 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_69d85a121520819093dcce999fdefe1a |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e0268608190947a58f559a67717 |
completed | April 16, 2026, 1:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff0b3f8eac81908489baeb4cb6bb53 |
completed | May 9, 2026, 10:23 a.m. |
Created at: April 10, 2026, 3:17 a.m.