Triple
T7281511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gülse Birsel |
E163159
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object |
Kanal D
Kanal D is a major Turkish television channel known for broadcasting popular series, entertainment programs, and news.
|
E654076
|
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: Kanal D | Statement: [Gülse Birsel, employer, Kanal D]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kanal D Context triple: [Gülse Birsel, employer, Kanal D]
-
A.
Canal 13
Canal 13 is a major Chilean free-to-air television network known for its news, entertainment, and long-running programming.
-
B.
Mashriq TV
Mashriq TV is a Pashto-language television channel known for broadcasting news, current affairs, and cultural programming to Pashto-speaking audiences.
-
C.
Star TV
Star TV is a major Asian satellite television network known for its broad entertainment and news programming across multiple countries.
-
D.
Estrella TV
Estrella TV is a Spanish-language American broadcast television network known for its variety of entertainment programming targeting Hispanic audiences in the United States.
-
E.
We TV
We TV is an American cable television network known for its reality programming focused on relationships, family life, and pop culture.
- 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: Kanal D Triple: [Gülse Birsel, employer, Kanal D]
Generated description
Kanal D is a major Turkish television channel known for broadcasting popular series, entertainment programs, and news.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kanal D Target entity description: Kanal D is a major Turkish television channel known for broadcasting popular series, entertainment programs, and news.
-
A.
Canal 13
Canal 13 is a major Chilean free-to-air television network known for its news, entertainment, and long-running programming.
-
B.
Mashriq TV
Mashriq TV is a Pashto-language television channel known for broadcasting news, current affairs, and cultural programming to Pashto-speaking audiences.
-
C.
Star TV
Star TV is a major Asian satellite television network known for its broad entertainment and news programming across multiple countries.
-
D.
Estrella TV
Estrella TV is a Spanish-language American broadcast television network known for its variety of entertainment programming targeting Hispanic audiences in the United States.
-
E.
We TV
We TV is an American cable television network known for its reality programming focused on relationships, family life, and pop culture.
- 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.