Triple
T18439210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jilin University |
E450480
|
entity |
| Predicate | alternativeName |
P39
|
FINISHED |
| Object | 吉林大学 |
—
|
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: 吉林大学 | Statement: [Jilin University, alternativeName, 吉林大学]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 吉林大学 Context triple: [Jilin University, alternativeName, 吉林大学]
-
A.
南开大学
南开大学 is a prestigious comprehensive research university in Tianjin, China, renowned for its strong academic tradition and influential alumni.
-
B.
Jilin University
chosen
Jilin University is a major national research university in Changchun, China, known for its comprehensive academic programs and strong emphasis on science and engineering.
-
C.
東北大学
東北大学は、日本の宮城県仙台市に本部を置く、研究力と工学・理学分野で特に高い評価を受ける国立総合大学です。
-
D.
西南大学
西南大学 is a comprehensive national university in Chongqing, China, known for its strengths in teacher education, agriculture, and the humanities and social sciences.
-
E.
Liaoning University
Liaoning University is a comprehensive public university in Shenyang, China, known for its strong programs in economics, law, and humanities.
- 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_69d8d381d6388190a9e94e9c658174e4 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e51c0ed6708190ae90efd8455ec352 |
completed | April 19, 2026, 6:16 p.m. |
Created at: April 10, 2026, 11:30 a.m.