Triple
T24277850
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | จังหวัดตรัง |
E605458
|
entity |
| Predicate | hasAreaRankInThailand |
P150581
|
FINISHED |
| Object | ลำดับที่ 46 โดยพื้นที่ |
—
|
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: ลำดับที่ 46 โดยพื้นที่ | Statement: [จังหวัดตรัง, hasAreaRankInThailand, ลำดับที่ 46 โดยพื้นที่]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAreaRankInThailand Context triple: [จังหวัดตรัง, hasAreaRankInThailand, ลำดับที่ 46 โดยพื้นที่]
-
A.
hasAreaRankInTaiwan
Indicates the relative ranking of an entity by its area size compared to other entities within Taiwan.
-
B.
hasRankByArea
chosen
Indicates that something is assigned a position or ranking based on its area size.
-
C.
rankByAreaInPhilippines
Indicates the relative ordering of entities based on their area size specifically within the Philippines.
-
D.
rankInChinaByArea
Indicates the position of an entity in an ordered list of entities in China when sorted by their area size.
-
E.
largestIslandOfThailand
Indicates that the subject is the island which has the greatest area among all islands in Thailand.
- 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_69e2954707dc8190915551eb114cfff6 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f28d60a454819093b46556966640ab |
completed | April 29, 2026, 10:59 p.m. |
| PD | Predicate disambiguation | batch_69f1c457a2908190993824395b3c365d |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 18, 2026, 12:07 a.m.