Triple
T11341433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aurora, Colorado |
E268606
|
entity |
| Predicate | rankInColoradoByPopulation |
P30027
|
FINISHED |
| Object | third-largest city in Colorado |
—
|
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: third-largest city in Colorado | Statement: [Aurora, Colorado, rankInColoradoByPopulation, third-largest city in Colorado]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInColoradoByPopulation Context triple: [Aurora, Colorado, rankInColoradoByPopulation, third-largest city in Colorado]
-
A.
rankByPopulationInColorado
chosen
Indicates the relative ordering of entities based on their population size within the state of Colorado.
-
B.
rankInKansasByPopulation
Indicates the position of an entity in an ordered list of Kansas entities based on their population size.
-
C.
rankInCaliforniaByPopulation
Indicates the ordinal position of an entity in a list of California entities ordered by their population size.
-
D.
countyNumberInColorado
Indicates that a given county is assigned a specific official county number within the state of Colorado.
-
E.
rankInArizonaByPopulation
Indicates the position an entity holds in a ranking of Arizona locations ordered by their population 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_69d6aacb1f0881908c84a349fd1be047 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7ea01c6c08190910a6ce8fb7e186d |
completed | April 9, 2026, 6:03 p.m. |
| PD | Predicate disambiguation | batch_69d787afe5a48190b8af1a3e19529641 |
completed | April 9, 2026, 11:04 a.m. |
Created at: April 8, 2026, 9:33 p.m.