Triple
T12915099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hynnekleiv Station |
E308959
|
entity |
| Predicate | servedPlace |
P3936
|
FINISHED |
| Object | Hynnekleiv |
E308953
|
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: Hynnekleiv | Statement: [Hynnekleiv Station, servedPlace, Hynnekleiv]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hynnekleiv Context triple: [Hynnekleiv Station, servedPlace, Hynnekleiv]
-
A.
Hynnekleiv
chosen
Hynnekleiv is a small village in the municipality of Froland in Agder county, southern Norway.
-
B.
Sæbraut
Sæbraut is a coastal road in Reykjavík, Iceland, known for its scenic waterfront views and public artworks along the shoreline.
-
C.
Fløya
Fløya is a Norwegian women's football club based in Tromsø that competes in the country's league system.
-
D.
Lærdalsøyri
Lærdalsøyri is a historic village in western Norway known for its well-preserved wooden buildings and location at the inner end of the Sognefjord.
-
E.
Namdalseid
Namdalseid is a former rural municipality in Trøndelag county, Norway, known for its forests, agriculture, and coastal landscape along the Namsenfjorden.
- 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_69d7bdf92b588190acdf2a2291ac4590 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971a0d6508190bca9668e9e06abfe |
completed | April 10, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6af5df0408190a8fe83cdd91e38c9 |
completed | May 3, 2026, 2:13 a.m. |
Created at: April 9, 2026, 5:41 p.m.