Triple
T25391225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Rāzna |
E636170
|
entity |
| Predicate | rankByAreaInLatvia |
P158260
|
FINISHED |
| Object | second largest lake in Latvia |
—
|
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 Latvia | Statement: [Lake Rāzna, rankByAreaInLatvia, second largest lake in Latvia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankByAreaInLatvia Context triple: [Lake Rāzna, rankByAreaInLatvia, second largest lake in Latvia]
-
A.
populationRankInLithuania
Indicates the relative position of an entity in terms of population size compared to other entities within Lithuania.
-
B.
areaRankingInEstonia
Indicates the relative position of something in a size-based ranking specifically within the context of Estonia.
-
C.
hasPopulationRankInEstonia
Indicates the relative position of an entity in the ordered list of populations within Estonia, such as its rank by population size compared to other entities in the country.
-
D.
rankInRussiaByArea
Indicates the position of an entity in an ordered list of entities in Russia sorted by their area size.
-
E.
rankInWorldByArea
Indicates the position of an entity in a global ordering based on its total area size.
- F. None of above. chosen
Provenance (4 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_69e75db263888190b77fff9e2827b9a2 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5657015648190bee3b56dceac3b10 |
completed | May 2, 2026, 2:46 a.m. |
| PD | Predicate disambiguation | batch_69f45d0dbc8c8190beecce679fce90a4 |
completed | May 1, 2026, 7:58 a.m. |
| PDg | Predicate description generation | batch_69f464ae42e88190b3549fdf4e0b425e |
completed | May 1, 2026, 8:30 a.m. |
Created at: April 21, 2026, 1:49 p.m.