Triple
T7468741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wels |
E176447
|
entity |
| Predicate | rankInStateByPopulation |
P21163
|
FINISHED |
| Object | second largest city in Upper Austria |
—
|
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 city in Upper Austria | Statement: [Wels, rankInStateByPopulation, second largest city in Upper Austria]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInStateByPopulation Context triple: [Wels, rankInStateByPopulation, second largest city in Upper Austria]
-
A.
rankByPopulationInUS
Indicates the relative ordering of entities based on the size of their populations within the United States.
-
B.
rankByPopulationInUnitedStates
Indicates the relative ordering of entities based on their population size within the United States.
-
C.
stateRank
chosen
Indicates the relative position or standing of an entity within a specific state-level ordering or hierarchy.
-
D.
populationDensityRankInUS
Indicates the relative position of a place in a ranking of U.S. locations ordered by population density.
-
E.
areaRankInUS
Indicates the relative position of an entity in a ranking of areas within the United States, based on its size.
- 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_69c69f223fd88190b4c69b95d7cbeeda |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f3f6f23881908e3e80b0c7335a15 |
completed | March 27, 2026, 9:17 p.m. |
| PD | Predicate disambiguation | batch_69c6f03d967081908a8e696ff9693b90 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:40 p.m.