Triple
T21943014
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xinhui |
E541867
|
entity |
| Predicate | borderedBy |
P224
|
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: [Xinhui, borderedBy, Taishan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taishan Context triple: [Xinhui, borderedBy, 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.
Mount Yu
Mount Yu is a prominent peak in Taiwan’s Central Mountain Range, renowned for its rugged alpine scenery and popularity among hikers and mountaineers.
- 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_69e0c47e2e5c81909a7f74ce3de50911 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1242515ec8190b015bf8c7b13be85 |
completed | April 28, 2026, 9:18 p.m. |
Created at: April 16, 2026, 7:56 p.m.