Triple
T28518747
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ka-tet |
E721697
|
entity |
| Predicate | exampleMember |
P168107
|
FINISHED |
| Object | Roland Deschain |
—
|
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: Roland Deschain | Statement: [ka-tet, exampleMember, Roland Deschain]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: exampleMember Context triple: [ka-tet, exampleMember, Roland Deschain]
-
A.
memberFrom
Indicates that an entity is a member originating from, or associated with, a particular source, group, or location.
-
B.
representsMember
Indicates that one entity is a member or constituent part of another entity, such as an individual belonging to a group or organization.
-
C.
includeMember
Indicates that one entity contains or has another entity as a member or part of its composition.
-
D.
boardMember
Indicates that a person serves on the governing board of an organization, participating in its oversight and decision-making.
-
E.
duoMember
Indicates that an entity is one of the two participants forming a duo with another entity.
- 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_69f01a5cbcc4819083fb4e723378713e |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69f673633d288190b52ceb9f8a057c44 |
completed | May 2, 2026, 9:57 p.m. |
| PD | Predicate disambiguation | batch_69f66ec5bf508190ad088b89455252bd |
completed | May 2, 2026, 9:38 p.m. |
| PDg | Predicate description generation | batch_69f67256d064819094be04fc1bbbc635 |
completed | May 2, 2026, 9:53 p.m. |
Created at: April 28, 2026, 3:19 a.m.