Triple
T3441739
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dalarna |
E72579
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Mora
Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
|
E356509
|
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: Mora | Statement: [Dalarna, contains, Mora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mora Context triple: [Dalarna, contains, Mora]
-
A.
Mora
Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
-
B.
Martos
Martos is a historic town in southern Spain’s Andalusia region, known for its olive oil production and hilltop setting dominated by a medieval castle.
-
C.
Morar
Morar is a small coastal village in the Lochaber area of the Scottish Highlands, known for its scenic beaches and proximity to the Road to the Isles.
-
D.
Kamorta
Kamorta is a significant inhabited island and settlement in India’s Nicobar archipelago, known for its strategic location and indigenous Nicobarese communities.
-
E.
Mauregard
Mauregard is a small commune in the Seine-et-Marne department of the Île-de-France region in north-central France, situated near Paris Charles de Gaulle Airport.
- 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: Mora Triple: [Dalarna, contains, Mora]
Generated description
Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mora Target entity description: Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
-
A.
Mora
Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
-
B.
Martos
Martos is a historic town in southern Spain’s Andalusia region, known for its olive oil production and hilltop setting dominated by a medieval castle.
-
C.
Morar
Morar is a small coastal village in the Lochaber area of the Scottish Highlands, known for its scenic beaches and proximity to the Road to the Isles.
-
D.
Kamorta
Kamorta is a significant inhabited island and settlement in India’s Nicobar archipelago, known for its strategic location and indigenous Nicobarese communities.
-
E.
Mauregard
Mauregard is a small commune in the Seine-et-Marne department of the Île-de-France region in north-central France, situated near Paris Charles de Gaulle Airport.
- 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_69ad85af50288190a854b76653deee6f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adba276b708190949f294a8d09ec7b |
completed | March 8, 2026, 6:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3548598088190907e13c88cb975fc |
completed | March 13, 2026, 12:04 a.m. |
| NEDg | Description generation | batch_69b355a8da148190896dacf746630445 |
completed | March 13, 2026, 12:09 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3561132888190b0439cd3d8e7bf96 |
completed | March 13, 2026, 12:10 a.m. |
Created at: March 8, 2026, 3:16 p.m.