Triple
T18946920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angarsk |
E463537
|
entity |
| Predicate | hasPopulationRankInIrkutskOblast |
P25930
|
FINISHED |
| Object | one of the largest cities |
—
|
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: one of the largest cities | Statement: [Angarsk, hasPopulationRankInIrkutskOblast, one of the largest cities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPopulationRankInIrkutskOblast Context triple: [Angarsk, hasPopulationRankInIrkutskOblast, one of the largest cities]
-
A.
hasPopulationRankInRegion
chosen
Indicates that an entity has a specific population-based rank or position within a defined geographic region.
-
B.
hasPopulationRank
Indicates the relative position of an entity in an ordered list based on the size of its population.
-
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.
populationRankingInUSSR
Indicates the relative position of an entity in terms of population size compared to other entities within the former USSR.
- 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_69d8dcfec90481909e926be9767e5779 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d5402ad881908add559249278895 |
completed | April 20, 2026, 7:26 a.m. |
| PD | Predicate disambiguation | batch_69e4a2efec5c8190840704016bf547a1 |
completed | April 19, 2026, 9:40 a.m. |
Created at: April 10, 2026, 11:59 a.m.