Triple
T30759182
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Esrum Lake |
E783180
|
entity |
| Predicate | rankingByAreaInDenmark |
P1170
|
FINISHED |
| Object | second-largest lake in Denmark |
—
|
LITERAL 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: second-largest lake in Denmark | Statement: [Esrum Lake, rankingByAreaInDenmark, second-largest lake in Denmark]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingByAreaInDenmark Context triple: [Esrum Lake, rankingByAreaInDenmark, second-largest lake in Denmark]
-
A.
rankInNorwayByArea
Indicates the position of an entity in an ordered list of areas within Norway, based on its size relative to others.
-
B.
rankInFinlandByArea
Indicates the position of an entity in an ordered list based on its area size within Finland.
-
C.
rankInGermanyByArea
Indicates the position of an entity in an ordered list based on its area size within Germany.
-
D.
chartPeakDenmark
Indicates that something reached its highest position on a music chart specifically in Denmark.
-
E.
areaRank
chosen
Indicates the relative ordering or position of an entity based on the size of its area compared to others.
- F. None of above.
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_69f224b047f48190b4f5efeb7ee97b37 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f74062b9388190b30546cf700a825c |
completed | May 3, 2026, 12:32 p.m. |
| PD | Predicate disambiguation | batch_69f73c802b848190b61a416b7488bd96 |
completed | May 3, 2026, 12:16 p.m. |
Created at: April 29, 2026, 8:39 p.m.