Triple
T18707327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grue |
E457404
|
entity |
| Predicate | hasValley |
P650
|
FINISHED |
| Object | Finnskogen |
—
|
NE NERFINISHED |
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: Finnskogen | Statement: [Grue, hasValley, Finnskogen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Finnskogen Context triple: [Grue, hasValley, Finnskogen]
-
A.
Finnskogen
chosen
Finnskogen is a forested region along the Norwegian-Swedish border known for its dense woodlands and historic Finnish immigrant culture.
-
B.
Hälsingland forests
Hälsingland forests are a vast, sparsely populated woodland region in central Sweden known for their boreal landscapes, wildlife, and traditional rural settlements.
-
C.
Kvamskogen
Kvamskogen is a popular mountainous recreational area in western Norway known for its ski resorts, cabins, and outdoor activities.
-
D.
Västra skogen
Västra skogen is a Stockholm metro station in Solna, Sweden, known for its deep underground platforms and distinctive cavern-style design.
-
E.
Skog
Skog is a small locality in Gävleborg County, Sweden, situated within Söderhamn Municipality.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d392aad081909fe31aa03e6e97d1 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e567185c648190848ca47498eb56b3 |
completed | April 19, 2026, 11:36 p.m. |
Created at: April 10, 2026, 11:50 a.m.