Triple
T4458962
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leksand |
E98202
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Mora |
E356509
|
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: Mora | Statement: [Leksand, locatedNear, Mora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mora Context triple: [Leksand, locatedNear, Mora]
-
A.
Mora
chosen
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.
-
B.
Mora
Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
-
C.
Mora
Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
-
D.
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.
-
E.
Odda
Odda is a town in western Norway known for its dramatic fjord landscape, industrial heritage, and proximity to popular hiking destinations like Trolltunga.
- 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_69b3454a7c608190944f5455c8031d73 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3567184f481908a2787e4ac9bb345 |
completed | March 13, 2026, 12:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b63767f8d08190a58cc441471adf90 |
completed | March 15, 2026, 4:36 a.m. |
Created at: March 12, 2026, 11:33 p.m.