Triple
T10173936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Du Yuming |
E235801
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Du |
E761297
|
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: Du | Statement: [Du Yuming, familyName, Du]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Du Context triple: [Du Yuming, familyName, Du]
-
A.
Du
chosen
Du is a Chinese surname historically borne by notable figures such as the Tang dynasty poet Du Fu.
-
B.
DU
DU is a premier public central university in India, renowned for its diverse academic programs and large collegiate system based in New Delhi.
-
C.
DU
DU is the vehicle registration code used on license plates for the German city of Duisburg.
-
D.
DU
DU is the commonly used abbreviation for the University of Dhaka, a leading public research university in Bangladesh.
-
E.
DU
DU is the commonly used abbreviation for the University of Denver, particularly associated with its Denver Pioneers athletic programs.
- 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_69ca84d1d5f88190ab878a1021ecff68 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdeca0dc508190916f2a1bbb288192 |
completed | April 2, 2026, 4:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d3010c386481908bc0c985c0b5ff93 |
completed | April 6, 2026, 12:40 a.m. |
Created at: March 30, 2026, 9:11 p.m.