Triple
T9729679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mehmet Akif Ersoy University |
E235705
|
entity |
| Predicate | hasNamesakeProfession |
P41835
|
FINISHED |
| Object | writer |
—
|
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: writer | Statement: [Mehmet Akif Ersoy University, hasNamesakeProfession, writer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNamesakeProfession Context triple: [Mehmet Akif Ersoy University, hasNamesakeProfession, writer]
-
A.
isNamedAfterOccupation
chosen
Indicates that an entity’s name is derived from or based on a particular occupation or profession.
-
B.
namesakeOccupation
Indicates that one entity’s occupation is the same as, or derived from, the occupation associated with the other entity’s namesake.
-
C.
hasNamesakeRoleInHistory
Indicates that an entity has a historical role or position that is the same as, or named after, another entity’s role in history.
-
D.
hasFamousNamesakeRole
Indicates that an entity has a role or position that shares its name with a well-known or historically notable person.
-
E.
hasFamousNamesake
Indicates that an entity shares its name with another well-known or notable 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_69ca84d0fad481909cdd45aa77416c48 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9eb0ff488190ac32ed304a3cd3bc |
completed | April 1, 2026, 10:39 p.m. |
| PD | Predicate disambiguation | batch_69cd03c6ffc88190a5e9569e19122ad5 |
completed | April 1, 2026, 11:38 a.m. |
Created at: March 30, 2026, 8:21 p.m.