Triple
T15040411
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Söderort |
E378584
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Björkhagen |
E1138040
|
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: Björkhagen | Statement: [Söderort, hasPart, Björkhagen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Björkhagen Context triple: [Söderort, hasPart, Björkhagen]
-
A.
Blackeberg
Blackeberg is a suburban district in western Stockholm, Sweden, best known internationally as the bleak, wintry backdrop of the Swedish vampire novel and film "Let the Right One In."
-
B.
Häggvik
Häggvik is a residential and commercial district in Sollentuna, part of the northern suburbs of Stockholm, Sweden.
-
C.
Eidskog
Eidskog is a rural municipality in Innlandet county, Norway, known for its forests, lakes, and location along the Swedish border.
-
D.
Bålsta
Bålsta is a locality in Uppsala County, Sweden, known as the main urban center of Håbo Municipality and a commuter town within the Greater Stockholm region.
-
E.
Fagersjö
chosen
Fagersjö is a residential district in southern Stockholm, Sweden, known for its proximity to lakes and green areas.
- 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_69ded82e79a481908ddb9609af8c4407 |
completed | April 15, 2026, 12:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69febfd954548190b3f7c60d95403f3e |
completed | May 9, 2026, 5:02 a.m. |
Created at: April 10, 2026, 3 a.m.