Triple
T35360494
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ken |
E1021461
|
entity |
| Predicate | relationshipToRay |
P206950
|
FINISHED |
| Object | mentor |
—
|
LITERAL 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: mentor | Statement: [Ken, relationshipToRay, mentor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToRay Context triple: [Ken, relationshipToRay, mentor]
-
A.
relationshipToRa
Indicates a specified type of relational connection that an entity has to the entity Ra.
-
B.
sceneRelationship
Indicates a contextual or spatial relationship that links entities based on how they co-occur or interact within the same scene.
-
C.
relationshipToRelative
Indicates the specific familial connection or kinship role that one person has in relation to a particular relative.
-
D.
relationshipToHeed
Indicates a relationship in which one entity is expected to pay attention to, respect, or follow the guidance, warnings, or wishes of another entity.
-
E.
termRelationTo
Indicates a general relational association between one term and another, without specifying the exact nature of that relationship.
- 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_69f76def44c881908a20e8008572eb44 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0324d08190ac5b610cc0f6a38c |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82179081908325a59b8539b3a8 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:03 p.m.