Triple
T35739604
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Master Mahmut |
E1032997
|
entity |
| Predicate | relationshipTypeWithCem |
P10690
|
FINISHED |
| Object | master-apprentice |
—
|
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: master-apprentice | Statement: [Master Mahmut, relationshipTypeWithCem, master-apprentice]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithCem Context triple: [Master Mahmut, relationshipTypeWithCem, master-apprentice]
-
A.
relationshipType
chosen
Indicates the specific kind of relationship that exists between two or more entities.
-
B.
demographicRelation
Indicates a relationship between entities based on demographic characteristics such as age, gender, ethnicity, or other population attributes.
-
C.
religiousRelation
Indicates a relationship between entities based on shared or differing religious affiliation, belief, practice, or institutional connection.
-
D.
termRelationTo
Indicates a general relational association between one term and another, without specifying the exact nature of that relationship.
-
E.
relationshipClaim
Indicates that one entity asserts or acknowledges the existence of a specific relationship between itself and 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_69f76e10e59081908d81ad9ce22f40b6 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037ce70f54819082946dad8d380825 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a069e6c8190857b611fffb7b867 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:05 p.m.