Triple
T19660054
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xiachuan Island |
E472056
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Taishan |
—
|
NE NERFINISHED |
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: Taishan | Statement: [Xiachuan Island, locatedIn, Taishan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taishan Context triple: [Xiachuan Island, locatedIn, Taishan]
-
A.
Taishan
chosen
Taishan is a county-level city in Guangdong Province, China, known as the ancestral homeland of many overseas Chinese and for its coastal scenery and historic villages.
-
B.
Taoshan
Taoshan is a prominent mountain peak in Taiwan known for its scenic alpine landscapes and popular hiking trails.
-
C.
Mount Yi
Mount Yi is a notable mountain in Shandong Province, China, known for its scenic landscapes and cultural-historical significance.
-
D.
Mount Tai
Mount Tai is one of China’s most famous and historically significant sacred mountains, revered in Chinese religion and culture for millennia.
-
E.
Dabajianshan
Dabajianshan is a prominent, steep-sided peak in Taiwan famed for its distinctive block-like summit and significance in indigenous culture and mountaineering.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51395348190ac1416d46dfc6db0 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e6414993ac8190b6da3702b1924b24 |
completed | April 20, 2026, 3:07 p.m. |
Created at: April 10, 2026, 1:45 p.m.