Triple
T7281514
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gülse Birsel |
E163159
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object |
NTV
NTV is a Turkish television news channel known for its 24-hour news coverage and influential role in Turkey’s media landscape.
|
E654078
|
NE FINISHED |
How this triple was built (4 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: NTV | Statement: [Gülse Birsel, employer, NTV]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: NTV Context triple: [Gülse Birsel, employer, NTV]
-
A.
NTV
NTV is a major Japanese commercial television network known for its wide range of news, entertainment, and sports programming.
-
B.
NTV (Russia)
NTV (Russia) is a major Russian television channel known for its nationwide broadcasting of news, entertainment, and investigative programs.
-
C.
ANO TV-Novosti
ANO TV-Novosti is a Russian state-funded media organization that operates the international television network RT (formerly Russia Today).
-
D.
NET TV
NET TV is a Catholic television network based in Brooklyn that produces and broadcasts religious and community programming for the Diocese of Brooklyn and surrounding areas.
-
E.
UNTV
UNTV is a Philippine television network known for its public service-oriented programming, news coverage, and religious content.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: NTV Triple: [Gülse Birsel, employer, NTV]
Generated description
NTV is a Turkish television news channel known for its 24-hour news coverage and influential role in Turkey’s media landscape.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: NTV Target entity description: NTV is a Turkish television news channel known for its 24-hour news coverage and influential role in Turkey’s media landscape.
-
A.
NTV
NTV is a major Japanese commercial television network known for its wide range of news, entertainment, and sports programming.
-
B.
NTV (Russia)
NTV (Russia) is a major Russian television channel known for its nationwide broadcasting of news, entertainment, and investigative programs.
-
C.
ANO TV-Novosti
ANO TV-Novosti is a Russian state-funded media organization that operates the international television network RT (formerly Russia Today).
-
D.
NET TV
NET TV is a Catholic television network based in Brooklyn that produces and broadcasts religious and community programming for the Diocese of Brooklyn and surrounding areas.
-
E.
UNTV
UNTV is a Philippine television network known for its public service-oriented programming, news coverage, and religious content.
- F. None of above. chosen
Provenance (5 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_69c6885c5964819085b209701769877f |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6eb34fe0c8190a642fd3339f0cacd |
completed | March 27, 2026, 8:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7db379e1c81908ebd4c44504ce5fb |
completed | March 28, 2026, 1:44 p.m. |
| NEDg | Description generation | batch_69c7df4788e081908ccc162125c6550d |
completed | March 28, 2026, 2:01 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7dfa826d081909129df80cca13daa |
completed | March 28, 2026, 2:03 p.m. |
Created at: March 27, 2026, 2:59 p.m.