Triple
T9729683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mehmet Akif Ersoy University |
E235705
|
entity |
| Predicate | hasNamesakeLanguage |
P90699
|
FINISHED |
| Object | Turkish |
—
|
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: Turkish | Statement: [Mehmet Akif Ersoy University, hasNamesakeLanguage, Turkish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNamesakeLanguage Context triple: [Mehmet Akif Ersoy University, hasNamesakeLanguage, Turkish]
-
A.
hasLanguageOfNickname
Indicates that an entity’s nickname is expressed in, or associated with, a particular language.
-
B.
hasRepresentativeLanguage
Indicates that an entity is associated with a language that serves as its primary or officially recognized means of representation or communication.
-
C.
hasLinguisticHeritage
Indicates that one entity possesses or is associated with the linguistic background, tradition, or ancestry of another entity.
-
D.
hasEndonymLanguage
Indicates that the language specified is the one in which a name or term is expressed in its own native or local form.
-
E.
hasLanguageOfOfficialName
Indicates that an entity’s official name is expressed in a specified language.
- 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_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. |
| PDg | Predicate description generation | batch_69cd07c5c978819084abc7267a5ced80 |
completed | April 1, 2026, 11:55 a.m. |
Created at: March 30, 2026, 8:21 p.m.