Triple
T2584782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yuan T. Lee |
E57173
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Yuan Tseh Lee |
E57173
|
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: Yuan Tseh Lee | Statement: [Yuan T. Lee, name, Yuan Tseh Lee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yuan Tseh Lee Context triple: [Yuan T. Lee, name, Yuan Tseh Lee]
-
A.
Yuan-Cheng Fung
Yuan-Cheng Fung was a pioneering bioengineer often regarded as the "father of modern biomechanics" for his foundational contributions to the mechanics of living tissues.
-
B.
Yuan T. Lee
chosen
Yuan T. Lee is a Taiwanese chemist and Nobel laureate renowned for his pioneering work in chemical reaction dynamics.
-
C.
Tung-Mow Yan
Tung-Mow Yan is a theoretical physicist best known for co-formulating the Drell–Yan process, a fundamental mechanism for lepton pair production in high-energy particle collisions.
-
D.
T. D. Lee
T. D. Lee is a Chinese-American physicist and Nobel laureate renowned for his pioneering work on parity violation in weak interactions.
-
E.
Y. W. Lee
Y. W. Lee was an academic mentor and electrical engineering scholar known for supervising future internet pioneer Leonard Kleinrock during his doctoral studies.
- 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_69ab4a4dca6481908c301f8e317396e7 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd3cd07588190b3cb8cc348f12938 |
completed | March 7, 2026, 7:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af83b4e09c8190909df4ba9b29d525 |
completed | March 10, 2026, 2:36 a.m. |
Created at: March 6, 2026, 9:49 p.m.