Triple
T15411770
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haakon the Good |
E368610
|
entity |
| Predicate | deathPlace |
P21
|
FINISHED |
| Object | Stord |
E378635
|
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: Stord | Statement: [Haakon the Good, deathPlace, Stord]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stord Context triple: [Haakon the Good, deathPlace, Stord]
-
A.
Stord
chosen
Stord is a large island and municipality in Vestland county, western Norway, known for its industrial activity and location along major fjords and shipping routes.
-
B.
Sandefjord
Sandefjord is a coastal town and municipality in southern Norway known for its maritime heritage, whaling history, and popular seaside attractions.
-
C.
Melbu
Melbu is a small coastal village and fishing community in Hadsel Municipality in Nordland county, Norway.
-
D.
Lyngdal
Lyngdal is a coastal town and municipality in southern Norway known for its beaches, fjords, and tourism.
-
E.
Skien
Skien is a historic city in southern Norway known as the birthplace of playwright Henrik Ibsen and as a regional commercial and industrial center.
- 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_69d85a16c68c819099c1b547fbc87b32 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03ea600b48190a3dbca1a68a2a1cd |
completed | April 16, 2026, 1:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff364f8ab481909f88e6b5fb42cd19 |
completed | May 9, 2026, 1:27 p.m. |
Created at: April 10, 2026, 3:20 a.m.