Triple
T32297280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Papa |
E825135
|
entity |
| Predicate | relationshipToSubject001 |
P84787
|
FINISHED |
| Object | mentor and controller of Henry Creel / One |
—
|
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 and controller of Henry Creel / One | Statement: [Papa, relationshipToSubject001, mentor and controller of Henry Creel / One]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToSubject001 Context triple: [Papa, relationshipToSubject001, mentor and controller of Henry Creel / One]
-
A.
subjectRelation
chosen
Indicates that one entity stands in a specified relational role or connection to another entity.
-
B.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
C.
relationshipToUser
Indicates the type of connection or association an entity has with the current user.
-
D.
addressesRelationship
Indicates that one entity directs communication, remarks, or attention specifically toward 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.
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_69f349115304819084ee91d345b6c8aa |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a037c894b488190bcbec2eccaff4a01 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379edf2d88190b492fca86ed23cac |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 12:44 a.m.