Triple
T653707
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nagoya |
E11598
|
entity |
| Predicate | twinnedWith |
P1072
|
FINISHED |
| Object | Nanjing |
E29741
|
NE 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: Nanjing | Statement: [Nagoya, twinnedWith, Nanjing]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nanjing Context triple: [Nagoya, twinnedWith, Nanjing]
-
A.
Nanjing
chosen
Nanjing is a major city in eastern China, historically significant as a former national capital and cultural center, and now an important political, economic, and educational hub on the Yangtze River.
-
B.
Changzhou
Changzhou is a major industrial and commercial city in Jiangsu Province, eastern China, known for its manufacturing base and location along the Yangtze River.
-
C.
Xuzhou
Xuzhou is a major historic city in northern Jiangsu Province, China, known as a strategic transportation hub and former battleground in multiple Chinese conflicts.
-
D.
Hefei
Hefei is the capital and largest city of Anhui Province in eastern China, known as a major industrial, scientific, and educational center.
-
E.
Shanghai
Shanghai is a major global financial hub and China’s largest city, known for its modern skyline, historic waterfront, and role as a center of international business and trade.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a4932862a0819098be659c814e4981 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f4a660c8190b887cb4da01ef7ae |
completed | March 1, 2026, 8:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a76d6cdc9c81909eb3240c00fa530d |
completed | March 3, 2026, 11:23 p.m. |
Created at: March 1, 2026, 7:36 p.m.