Triple
T13964621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rudge |
E335890
|
entity |
| Predicate | relationshipWithTeacher |
P18487
|
FINISHED |
| Object | student of Hector |
—
|
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: student of Hector | Statement: [Rudge, relationshipWithTeacher, student of Hector]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithTeacher Context triple: [Rudge, relationshipWithTeacher, student of Hector]
-
A.
relationshipToStudents
Indicates the type or nature of connection one entity has with students, such as role, affiliation, or responsibility toward them.
-
B.
relationshipToUniversity
Indicates the type or nature of a person's or entity's connection or affiliation with a specific university.
-
C.
relationshipToHeadmaster
Indicates the specific type of personal or professional relationship an entity has with the headmaster.
-
D.
notableStudentOrMentorRelationship
chosen
Indicates a notable educational or mentorship relationship between two individuals, where one has been a significant student or mentor of the other.
-
E.
hasTeacher
Indicates that one entity serves as an instructor or educator for another entity.
- 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_69d81c61f3508190aaf2ca0dc0002c59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e7e24f08190ba939a8044860033 |
completed | April 14, 2026, 12:09 p.m. |
| PD | Predicate disambiguation | batch_69dd465a21408190b912a42c50ffa0d9 |
completed | April 13, 2026, 7:39 p.m. |
Created at: April 9, 2026, 10:18 p.m.