Triple
T14436509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | collaborating with Fred Rogers |
E357976
|
entity |
| Predicate | involvesRoleOfJoanneRogers |
P114268
|
FINISHED |
| Object | advisor |
—
|
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: advisor | Statement: [collaborating with Fred Rogers, involvesRoleOfJoanneRogers, advisor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvesRoleOfJoanneRogers Context triple: [collaborating with Fred Rogers, involvesRoleOfJoanneRogers, advisor]
-
A.
roleOfJennifer Morris
Indicates that Jennifer Morris holds or performs a particular role, position, or function in relation to another entity or context.
-
B.
hasGingerRogersRole
Indicates that an entity is assigned or associated with a role specifically identified as the "Ginger Rogers" role in a given context or production.
-
C.
roleOfJoyceKinney
Indicates that the specified role or character is associated with or portrayed by Joyce Kinney.
-
D.
relationshipToJoannaKramer
Indicates the specific familial, social, or interpersonal connection that one entity has to Joanna Kramer.
-
E.
roleInvolves
Indicates that a particular role includes or requires participation in a specified activity, responsibility, 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_69d8279402a88190821ffa39ae15bccf |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de9148cf4481909082cc91b2f76218 |
completed | April 14, 2026, 7:11 p.m. |
| PD | Predicate disambiguation | batch_69de5c3a02fc819097373f97a260cdeb |
completed | April 14, 2026, 3:24 p.m. |
| PDg | Predicate description generation | batch_69de5fb4de14819092acdecbd201d672 |
completed | April 14, 2026, 3:39 p.m. |
Created at: April 10, 2026, 1:18 a.m.