Triple
T7717819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lienchiang County |
E174930
|
entity |
| Predicate | hasAreaRankInTaiwan |
P78322
|
FINISHED |
| Object | one of the smallest counties by land area |
—
|
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 smallest counties by land area | Statement: [Lienchiang County, hasAreaRankInTaiwan, one of the smallest counties by land area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAreaRankInTaiwan Context triple: [Lienchiang County, hasAreaRankInTaiwan, one of the smallest counties by land area]
-
A.
rankInChinaByArea
Indicates the position of an entity in an ordered list of entities in China when sorted by their area size.
-
B.
distanceFromTaiwanMainIsland
Indicates the measured spatial distance between an entity’s location and the main island of Taiwan.
-
C.
rankByAreaInPhilippines
Indicates the relative ordering of entities based on their area size specifically within the Philippines.
-
D.
areaRankInMalaysia
Indicates the relative position of an entity in a size-based ranking by area within Malaysia.
-
E.
hasLandmarkArea
Indicates that a specified area is designated as the landmark area associated with a particular entity or location.
- 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_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. |
| PDg | Predicate description generation | batch_69c702e9a32081909a153190a62af426 |
completed | March 27, 2026, 10:21 p.m. |
Created at: March 27, 2026, 4:05 p.m.