Triple
T18500932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emmental |
E452062
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object |
Seeberg
Seeberg is a small Swiss municipality located in the Emmental region of the canton of Bern.
|
E1327586
|
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: Seeberg | Statement: [Emmental, containsSettlement, Seeberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seeberg Context triple: [Emmental, containsSettlement, Seeberg]
-
A.
Sundsberg
Sundsberg is a residential village and district in the municipality of Kirkkonummi in southern Finland, known for its modern housing and proximity to the Helsinki metropolitan area.
-
B.
Moberg
Moberg is a Swedish surname most famously associated with Vilhelm Moberg, the renowned author of the "The Emigrants" series about Swedish migration to America.
-
C.
Solberga
Solberga is a residential district in southern Stockholm, Sweden, known for its mid-20th-century housing and green areas.
-
D.
Spydeberg
Spydeberg is a small Norwegian village and former municipality in the traditional region of Østfold, known for its rural character and historic church.
-
E.
Gyllensten
Gyllensten is a Swedish surname most notably associated with Lars Gyllensten, a prominent author and former member of the Swedish Academy.
- 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: Seeberg Triple: [Emmental, containsSettlement, Seeberg]
Generated description
Seeberg is a small Swiss municipality located in the Emmental region of the canton of Bern.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Seeberg Target entity description: Seeberg is a small Swiss municipality located in the Emmental region of the canton of Bern.
-
A.
Sundsberg
Sundsberg is a residential village and district in the municipality of Kirkkonummi in southern Finland, known for its modern housing and proximity to the Helsinki metropolitan area.
-
B.
Moberg
Moberg is a Swedish surname most famously associated with Vilhelm Moberg, the renowned author of the "The Emigrants" series about Swedish migration to America.
-
C.
Solberga
Solberga is a residential district in southern Stockholm, Sweden, known for its mid-20th-century housing and green areas.
-
D.
Spydeberg
Spydeberg is a small Norwegian village and former municipality in the traditional region of Østfold, known for its rural character and historic church.
-
E.
Gyllensten
Gyllensten is a Swedish surname most notably associated with Lars Gyllensten, a prominent author and former member of the Swedish Academy.
- 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_69d8d3855d50819097fc8561b0299dd9 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e532c43de48190b49b87c1bb591016 |
completed | April 19, 2026, 7:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a047141e8208190b240a11ae3b02ec0 |
completed | May 13, 2026, 12:40 p.m. |
| NEDg | Description generation | batch_6a04738415508190945eec50944d96e9 |
completed | May 13, 2026, 12:50 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a04746a380c819097df59bdbce0f6f6 |
completed | May 13, 2026, 12:54 p.m. |
Created at: April 10, 2026, 11:36 a.m.