Triple
T5669232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steinkjer |
E124933
|
entity |
| Predicate | mergedWith |
P77
|
FINISHED |
| Object |
Beitstad
Beitstad was a former municipality in Trøndelag county, Norway, that later became part of the town and municipality of Steinkjer.
|
E575146
|
NE FINISHED |
How this triple was built (4 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: Beitstad | Statement: [Steinkjer, mergedWith, Beitstad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beitstad Context triple: [Steinkjer, mergedWith, Beitstad]
-
A.
Sarpsborg
Sarpsborg is a historic city and municipality in Viken county, Norway, known as one of the country’s oldest towns and an important industrial and administrative center in the Østfold region.
-
B.
Lørenskog
Lørenskog is a suburban municipality in Viken county, Norway, located just east of Oslo and known for its residential areas and commercial centers.
-
C.
Rakkestad
Rakkestad is a rural municipality in Viken county, southeastern Norway, known for its agriculture and forests.
-
D.
Hønefoss
Hønefoss is a Norwegian town known as a regional commercial and transport hub, situated along the Begna River northwest of Oslo.
-
E.
Bjug Harstad
Bjug Harstad was a Norwegian-American Lutheran minister and educator best known for establishing Pacific Lutheran University in Washington State.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Beitstad Triple: [Steinkjer, mergedWith, Beitstad]
Generated description
Beitstad was a former municipality in Trøndelag county, Norway, that later became part of the town and municipality of Steinkjer.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Beitstad Target entity description: Beitstad was a former municipality in Trøndelag county, Norway, that later became part of the town and municipality of Steinkjer.
-
A.
Sarpsborg
Sarpsborg is a historic city and municipality in Viken county, Norway, known as one of the country’s oldest towns and an important industrial and administrative center in the Østfold region.
-
B.
Lørenskog
Lørenskog is a suburban municipality in Viken county, Norway, located just east of Oslo and known for its residential areas and commercial centers.
-
C.
Rakkestad
Rakkestad is a rural municipality in Viken county, southeastern Norway, known for its agriculture and forests.
-
D.
Hønefoss
Hønefoss is a Norwegian town known as a regional commercial and transport hub, situated along the Begna River northwest of Oslo.
-
E.
Bjug Harstad
Bjug Harstad was a Norwegian-American Lutheran minister and educator best known for establishing Pacific Lutheran University in Washington State.
- F. None of above. chosen
Provenance (5 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_69c00828906881908966f270b8f130cf |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0234891d48190bf662f38ef84d4f3 |
completed | March 22, 2026, 5:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c16e69e9188190a4dd94c34657a74f |
completed | March 23, 2026, 4:46 p.m. |
| NEDg | Description generation | batch_69c1e248fa748190b15a92135e67d420 |
completed | March 24, 2026, 1 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c1e3323f788190a8cc4c870fef1d2b |
completed | March 24, 2026, 1:04 a.m. |
Created at: March 22, 2026, 3:43 p.m.