Triple
T17615011
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alstahaug |
E429059
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Vega |
—
|
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: Vega | Statement: [Alstahaug, borders, Vega]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vega Context triple: [Alstahaug, borders, Vega]
-
A.
Vega
Vega is a European small-lift launch vehicle developed by the European Space Agency and partners, primarily used to place light payloads into low Earth orbit.
-
B.
Vega
chosen
Vega is a Norwegian island renowned for its UNESCO-listed archipelago, traditional eiderdown harvesting, and rich coastal birdlife.
-
C.
Vega
Vega is an open-source visualization grammar and toolkit for creating, sharing, and exploring interactive data visualizations in a declarative JSON format.
-
D.
Vega
Vega is a common Spanish surname borne by numerous notable individuals across fields such as entertainment, sports, and politics.
-
E.
Vega
Vega is a residential locality in Haninge Municipality, Stockholm County, Sweden, known for its commuter rail station and growing suburban housing developments.
- 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46d3174008190a2b5bb1b061ea4df |
completed | April 19, 2026, 5:50 a.m. |
Created at: April 10, 2026, 5:51 a.m.