Triple
T11801449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leshan |
E280634
|
entity |
| Predicate | hasPopulationRankInSichuan |
P25930
|
FINISHED |
| Object | one of the medium-sized cities |
—
|
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: one of the medium-sized cities | Statement: [Leshan, hasPopulationRankInSichuan, one of the medium-sized cities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPopulationRankInSichuan Context triple: [Leshan, hasPopulationRankInSichuan, one of the medium-sized cities]
-
A.
hasPopulationRank
Indicates the relative position of an entity in an ordered list based on the size of its population.
-
B.
hasPopulationRankInRegion
chosen
Indicates that an entity has a specific population-based rank or position within a defined geographic region.
-
C.
rankInChinaByArea
Indicates the position of an entity in an ordered list of entities in China when sorted by their area size.
-
D.
hasCityRank
Indicates that a city holds a particular rank or position within a defined ordering or hierarchy (such as size, importance, or administrative level).
-
E.
populationRankInQinghai
Indicates the relative position of an entity in a ranking ordered by population size within Qinghai.
- 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_69d6ab258b808190b1735835c841e3a4 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a658f918819092c2db05fe2ab0ce |
completed | April 10, 2026, 7:27 a.m. |
| PD | Predicate disambiguation | batch_69d8a24e9a088190aff7932d1ff93dbf |
completed | April 10, 2026, 7:10 a.m. |
Created at: April 8, 2026, 9:42 p.m.