Triple
T15030211
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green line (Stockholm metro) |
E378322
|
entity |
| Predicate | hasTerminus |
P388
|
FINISHED |
| Object | Alvik |
E379961
|
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: Alvik | Statement: [Green line (Stockholm metro), hasTerminus, Alvik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alvik Context triple: [Green line (Stockholm metro), hasTerminus, Alvik]
-
A.
Alvik
chosen
Alvik is a district in western Stockholm known as a key public transport hub, particularly for its tram and metro connections.
-
B.
Ahlberg
Ahlberg is a Swedish surname borne by various notable individuals in fields such as film, literature, and the arts.
-
C.
Arvegil
Arvegil is a fictional Dúnedain king of Arnor in J.R.R. Tolkien’s legendarium, descended from Isildur.
-
D.
Larsmo
Larsmo is a coastal municipality in western Finland known for its archipelago and Swedish-speaking majority population.
-
E.
Aulnat
Aulnat is a commune in central France situated in the Puy-de-Dôme department within the Auvergne region.
- 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_69d85cd46b2c819090d054c27787f677 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7e2416081908dfba48d7f7b4a84 |
completed | April 15, 2026, 12:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9dd967588190821cf47e9734db21 |
completed | May 9, 2026, 2:37 a.m. |
Created at: April 10, 2026, 2:59 a.m.