Triple
T28046893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yu the Great |
E708708
|
entity |
| Predicate | mentorOrAppointer |
P163660
|
FINISHED |
| Object | Shun |
—
|
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: Shun | Statement: [Yu the Great, mentorOrAppointer, Shun]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mentorOrAppointer Context triple: [Yu the Great, mentorOrAppointer, Shun]
-
A.
mentorOrPartner
Indicates a relationship in which one entity either provides guidance and support to another as a mentor or collaborates with them on relatively equal footing as a partner.
-
B.
advisorOf
Indicates that one entity serves as an advisor, providing guidance or counsel, to another entity.
-
C.
trainer
Indicates a relationship where one entity teaches, coaches, or prepares another entity to develop skills, knowledge, or performance in a particular domain.
-
D.
mentorCharacter
Indicates that one character serves as a mentor, providing guidance, teaching, or support to another character.
-
E.
hasAppointer
Indicates that one entity is responsible for appointing or assigning another entity to a role, position, or function.
- F. None of above. chosen
Provenance (4 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_69ef9b6cf538819094a633ffa67afec1 |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69f63f342abc8190bc64e5d54d0ddacf |
completed | May 2, 2026, 6:15 p.m. |
| PD | Predicate disambiguation | batch_69f63710d17c819084cfe96e6df334fd |
completed | May 2, 2026, 5:40 p.m. |
| PDg | Predicate description generation | batch_69f63893cc188190883ac9321a95d2dc |
completed | May 2, 2026, 5:46 p.m. |
Created at: April 27, 2026, 8:30 p.m.