Triple
T37061062
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 京都府長岡京市 |
E917324
|
entity |
| Predicate | 人口の傾向 |
P31774
|
FINISHED |
| Object | 京都市・大阪市への通勤通学者が多い |
—
|
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: 京都市・大阪市への通勤通学者が多い | Statement: [京都府長岡京市, 人口の傾向, 京都市・大阪市への通勤通学者が多い]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 人口の傾向 Context triple: [京都府長岡京市, 人口の傾向, 京都市・大阪市への通勤通学者が多い]
-
A.
population
Indicates the total number of individuals living in or present within a specified area or group.
-
B.
approximatePopulationTrend
chosen
Indicates an estimated or generalized pattern of how a population changes over time (e.g., increasing, decreasing, or stable) rather than an exact count.
-
C.
populationOutcome
Indicates the resulting state, condition, or effect experienced by a population as a consequence of a specified exposure, intervention, or circumstance.
-
D.
demographicImpact
Indicates how an action, event, or condition affects the size, structure, or composition of a population.
-
E.
demographics
Indicates the relationship of providing or characterizing statistical information about a population’s attributes, such as age, gender, income, or education.
- 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_69f76e95fa40819091e14681087ae5e4 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb34e5576881909394355c8ec6ddd2 |
completed | May 6, 2026, 12:32 p.m. |
| PD | Predicate disambiguation | batch_69fb2f6171e88190bf1e0ee6a644b6a9 |
completed | May 6, 2026, 12:09 p.m. |
Created at: May 3, 2026, 4:14 p.m.