Triple
T8158161
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guan Yu |
E190504
|
entity |
| Predicate | posthumousTitle |
P4225
|
FINISHED |
| Object | Lord Guan |
E190504
|
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: Lord Guan | Statement: [Guan Yu, posthumousTitle, Lord Guan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lord Guan Context triple: [Guan Yu, posthumousTitle, Lord Guan]
-
A.
Lord Shang
Lord Shang was an influential Chinese statesman and legalist reformer of the Warring States period, best known for transforming the state of Qin into a highly centralized and powerful military state.
-
B.
Guan Yu
chosen
Guan Yu was a famed Chinese general and deified warrior known for his loyalty, martial prowess, and central role in the historical and literary accounts of the late Eastern Han and Three Kingdoms era.
-
C.
Khương Đình Ward
Khương Đình Ward is an urban administrative subdivision of Thanh Xuân District in Hanoi, Vietnam.
-
D.
Sima Kang
Sima Kang was a historical figure associated with the compilation of the Chinese chronicle Zizhi Tongjian, contributing to its editorial work.
-
E.
Shu Chien
Shu Chien is a renowned Chinese-American physiologist and bioengineer recognized for pioneering contributions to cardiovascular biomechanics and microcirculation research.
- 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_69ca82bfeb6481909d07b91b5cf69f59 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb44da14a481909f8d3277762b0e75 |
completed | March 31, 2026, 3:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cdc6804a0c819091a6f46ef6c5670d |
completed | April 2, 2026, 1:29 a.m. |
Created at: March 30, 2026, 5:38 p.m.