Triple
T84668
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grand Rapids, Michigan |
E1703
|
entity |
| Predicate | populationRankInMichigan |
P1169
|
FINISHED |
| Object | second-largest city in Michigan |
—
|
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 Michigan | Statement: [Grand Rapids, Michigan, populationRankInMichigan, second-largest city in Michigan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: populationRankInMichigan Context triple: [Grand Rapids, Michigan, populationRankInMichigan, second-largest city in Michigan]
-
A.
areaRankInUS
Indicates the relative position of an entity in a ranking of areas within the United States, based on its size.
-
B.
rankByPopulationInUS
Indicates the relative ordering of entities based on the size of their populations within the United States.
-
C.
rankByPopulationInUnitedStates
Indicates the relative ordering of entities based on their population size within the United States.
-
D.
populationRankInTexas
Indicates the relative position of an entity in terms of population size compared to other entities within Texas.
-
E.
populationRank
chosen
Indicates the relative position of an entity in an ordered list based on the size of its population.
- 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_69a24c8150408190910a693eb51c1f71 |
completed | Feb. 28, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69a24f4e73c081908d2da146226ef05e |
completed | Feb. 28, 2026, 2:13 a.m. |
| PD | Predicate disambiguation | batch_69a24eb469548190b38c24e81f36c838 |
completed | Feb. 28, 2026, 2:11 a.m. |
Created at: Feb. 28, 2026, 2:06 a.m.