Triple
T9008790
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eric Liddell |
E215412
|
entity |
| Predicate | workedIn |
P1527
|
FINISHED |
| Object | Xiaochang |
E215412
|
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: Xiaochang | Statement: [Eric Liddell, workedIn, Xiaochang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Xiaochang Context triple: [Eric Liddell, workedIn, Xiaochang]
-
A.
Xiaochang
chosen
Xiaochang is a county in Hubei Province, China, known historically as a rural mission and teaching post where figures like Eric Liddell worked.
-
B.
Chongxi
Chongxi is the given name of Bai Chongxi, a prominent Chinese Muslim general and political figure of the Republic of China.
-
C.
Yuxiang
Yuxiang is a Chinese given name notably borne by the early 20th-century warlord and military leader Feng Yuxiang.
-
D.
Linxiang
Linxiang is a county-level city administered by Yueyang in Hunan Province, China, known for its location near the Yangtze River and its regional agricultural and industrial activities.
-
E.
Wenzhong
Wenzhong is the posthumous honorific title granted to the eminent Song dynasty scholar-official, historian, and poet Ouyang Xiu.
- 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_69ca83a2bf088190986ee7a8eb90407d |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc69bed8588190afc9cbca12b75a3b |
completed | April 1, 2026, 12:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfdb9a11948190a43f60d0df71b1af |
completed | April 3, 2026, 3:24 p.m. |
Created at: March 30, 2026, 7:06 p.m.