Triple
T31964067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Counties of Tennessee |
E816126
|
entity |
| Predicate | secondHighestCountyPopulation |
P196954
|
FINISHED |
| Object | Davidson County |
—
|
NE NERFINISHED |
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: Davidson County | Statement: [Counties of Tennessee, secondHighestCountyPopulation, Davidson County]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: secondHighestCountyPopulation Context triple: [Counties of Tennessee, secondHighestCountyPopulation, Davidson County]
-
A.
thirdHighestCountyPopulation
Indicates that the referenced county has the third highest population among a specified set or within a given region.
-
B.
secondLargestStateByPopulation
Indicates that the subject is the state with the second-highest population among all states in the specified context.
-
C.
fourthHighestCountyPopulation
Indicates that the referenced county has the fourth-largest population among a specified set of counties.
-
D.
mostPopulousCountyIn
Indicates that the subject is the county with the largest population within the specified object region or jurisdiction.
-
E.
secondLargestTownIn
Indicates that one town is the second largest town (by size or population) within a specified region or administrative area.
- 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_69f348f5ae5481909da0247869f51955 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fe6fea4a288190bf8615c5d6bf41b4 |
completed | May 8, 2026, 11:21 p.m. |
| PD | Predicate disambiguation | batch_69fe6f774de08190975a2393b9a1fd22 |
completed | May 8, 2026, 11:19 p.m. |
| PDg | Predicate description generation | batch_69fe6fe98e38819085100ce4c6cee5b8 |
completed | May 8, 2026, 11:21 p.m. |
Created at: May 1, 2026, 12:09 a.m.