Triple
T405698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Takatsuki |
E9377
|
entity |
| Predicate | hasPopulationRankInOsakaPrefecture |
P1026
|
FINISHED |
| Object | mid-sized city |
—
|
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: mid-sized city | Statement: [Takatsuki, hasPopulationRankInOsakaPrefecture, mid-sized city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPopulationRankInOsakaPrefecture Context triple: [Takatsuki, hasPopulationRankInOsakaPrefecture, mid-sized city]
-
A.
hasPopulationRank
chosen
Indicates the relative position of an entity in an ordered list based on the size of its population.
-
B.
gdpRankInJapan
Indicates the position of an entity in the ordered ranking of GDP values within Japan.
-
C.
hasPrefecture
Indicates that one administrative region or country possesses or is associated with a specific prefecture as a subordinate territorial unit.
-
D.
hasPopulationAsOf
Indicates that a population count is associated with a specific point or date in time when that population figure was valid or recorded.
-
E.
hasPopulationApproximate
Indicates that an entity has an estimated or approximate population size, rather than an exact count.
- 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_69a2e8004cb88190b92ed1add6abf41a |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2ecbc00508190bbb602179273f29c |
completed | Feb. 28, 2026, 1:25 p.m. |
| PD | Predicate disambiguation | batch_69a2e97066e8819083cc1b3a421b9650 |
completed | Feb. 28, 2026, 1:11 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.