Triple
T11460219
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vilhelm Blomgren |
E271634
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Moa Gammel
Moa Gammel is a Swedish actress and producer known for her work in film, television, and radio drama.
|
E927753
|
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: Moa Gammel | Statement: [Vilhelm Blomgren, spouse, Moa Gammel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moa Gammel Context triple: [Vilhelm Blomgren, spouse, Moa Gammel]
-
A.
Hafslund
Hafslund is a major Norwegian energy and utility company known for its role in electricity production, distribution, and related services.
-
B.
Alstahaug
Alstahaug is a coastal municipality in northern Norway known for its historic church, island landscapes, and maritime heritage.
-
C.
Avaldsnes
Avaldsnes is a historic village in Rogaland county, Norway, known as one of the country’s oldest royal seats and a key center in Viking-era history.
-
D.
Storslett
Storslett is a small village and administrative center in Nordreisa Municipality in Troms og Finnmark county in northern Norway.
-
E.
Thyborøn
Thyborøn is a coastal fishing town and tourist destination in western Jutland, Denmark, known for its harbor, North Sea beaches, and World War II coastal fortifications.
- 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: Moa Gammel Triple: [Vilhelm Blomgren, spouse, Moa Gammel]
Generated description
Moa Gammel is a Swedish actress and producer known for her work in film, television, and radio drama.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Moa Gammel Target entity description: Moa Gammel is a Swedish actress and producer known for her work in film, television, and radio drama.
-
A.
Hafslund
Hafslund is a major Norwegian energy and utility company known for its role in electricity production, distribution, and related services.
-
B.
Alstahaug
Alstahaug is a coastal municipality in northern Norway known for its historic church, island landscapes, and maritime heritage.
-
C.
Avaldsnes
Avaldsnes is a historic village in Rogaland county, Norway, known as one of the country’s oldest royal seats and a key center in Viking-era history.
-
D.
Storslett
Storslett is a small village and administrative center in Nordreisa Municipality in Troms og Finnmark county in northern Norway.
-
E.
Thyborøn
Thyborøn is a coastal fishing town and tourist destination in western Jutland, Denmark, known for its harbor, North Sea beaches, and World War II coastal fortifications.
- 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_69d6aadff8888190a13f253f0d460874 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d822f384f08190b1150ed1389dd31a |
completed | April 9, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5e91f1bb881909a9c36d837e4059b |
completed | April 20, 2026, 8:51 a.m. |
| NEDg | Description generation | batch_69e5f1593c2c8190885f80ad5eeba3ec |
completed | April 20, 2026, 9:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e5f87bbd988190ac388a3c34b2e95a |
completed | April 20, 2026, 9:57 a.m. |
Created at: April 8, 2026, 9:35 p.m.