Triple
T7717818
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lienchiang County |
E174930
|
entity |
| Predicate | hasPopulationRankInTaiwan |
P25930
|
FINISHED |
| Object | one of the least populous counties |
—
|
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 least populous counties | Statement: [Lienchiang County, hasPopulationRankInTaiwan, one of the least populous counties]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPopulationRankInTaiwan Context triple: [Lienchiang County, hasPopulationRankInTaiwan, one of the least populous counties]
-
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.
areaRankInMalaysia
Indicates the relative position of an entity in a size-based ranking by area within Malaysia.
-
D.
populationRankInVietnam
Indicates the relative position of an entity in terms of population size compared to other entities within Vietnam.
-
E.
hasPopulationRankInTasmania
Indicates the relative position of an entity in the ordered list of population sizes within Tasmania.
- 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_69c6995c463c8190a14458036249d419 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702ebb7448190ae8d47fe0cbb0907 |
completed | March 27, 2026, 10:21 p.m. |
| PD | Predicate disambiguation | batch_69c701683dec8190be9861e592aa8ce0 |
completed | March 27, 2026, 10:15 p.m. |
Created at: March 27, 2026, 4:05 p.m.