Triple
T37223946
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shirley the Loon |
E922957
|
entity |
| Predicate | mentorAnalogOf |
P106997
|
FINISHED |
| Object | Melissa Duck |
—
|
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: Melissa Duck | Statement: [Shirley the Loon, mentorAnalogOf, Melissa Duck]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mentorAnalogOf Context triple: [Shirley the Loon, mentorAnalogOf, Melissa Duck]
-
A.
mentorOrAppointer
Indicates a relationship where one entity either serves as a mentor to another or is responsible for appointing that entity to a role or position.
-
B.
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.
-
C.
mentorType
Indicates the specific role or category of mentorship that one entity provides to another.
-
D.
mentorshipModel
Indicates a relationship where one entity provides guidance, support, and expertise to help another entity develop skills, knowledge, or professional growth.
-
E.
mentorCharacter
chosen
Indicates that one character serves as a mentor, providing guidance, teaching, or support to another character.
- F. None of above.
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_69f76ea7f0008190b31b8e30f3d05a71 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fb4134225081909fd60703b8cae397 |
completed | May 6, 2026, 1:25 p.m. |
| PD | Predicate disambiguation | batch_69fb35bf767081908de8345358ca7f44 |
completed | May 6, 2026, 12:36 p.m. |
Created at: May 3, 2026, 4:15 p.m.