Triple
T15427334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Horsens |
E369544
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Halmstad |
E218044
|
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: Halmstad | Statement: [Horsens, hasTwinTown, Halmstad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Halmstad Context triple: [Horsens, hasTwinTown, Halmstad]
-
A.
Halmstad
chosen
Halmstad is a coastal city in southwestern Sweden known for its historic town center, harbor, and role as a strategic site in Scandinavian conflicts.
-
B.
Halmstad
Halmstad is a village in Moss municipality in Viken county, southeastern Norway.
-
C.
Kristianstad
Kristianstad is a historic city in southern Sweden known for its well-preserved Renaissance architecture and proximity to the wetlands of the Kristianstad Vattenrike Biosphere Reserve.
-
D.
Sundsvall
Sundsvall is a coastal city in central Sweden known as an important industrial and commercial center on the Gulf of Bothnia.
-
E.
Ystad
Ystad is a historic coastal town in southern Sweden known for its medieval architecture and as the setting of Henning Mankell’s Kurt Wallander crime novels.
- 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_69d85a1849f48190bf898068b2806fae |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ec31f4881908b26ff7c381d7bc9 |
completed | April 16, 2026, 1:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00bafb761881908e7a891ef390982a |
completed | May 10, 2026, 5:06 p.m. |
Created at: April 10, 2026, 3:20 a.m.