Triple
T27032726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Selvi Boylum Al Yazmalım |
E680965
|
entity |
| Predicate | leadActorRole |
P5563
|
FINISHED |
| Object |
Kadir İnanır as İlyas
Kadir İnanır as İlyas is the iconic portrayal of a passionate truck driver whose complex love story with Asya forms the emotional core of the classic Turkish film "Selvi Boylum Al Yazmalım."
|
E1755077
|
NE 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: Kadir İnanır as İlyas | Statement: [Selvi Boylum Al Yazmalım, leadActorRole, Kadir İnanır as İlyas]
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: Kadir İnanır as İlyas Triple: [Selvi Boylum Al Yazmalım, leadActorRole, Kadir İnanır as İlyas]
Generated description
Kadir İnanır as İlyas is the iconic portrayal of a passionate truck driver whose complex love story with Asya forms the emotional core of the classic Turkish film "Selvi Boylum Al Yazmalım."
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_69eeeb5566f08190813daf896fa3da04 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f6223766208190a606c293dd7bb250 |
completed | May 2, 2026, 4:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a123abc4008819086c50a92d54db375 |
completed | May 23, 2026, 11:39 p.m. |
| NEDg | Description generation | batch_6a123ba9aea081909f20ff78ab91747e |
completed | May 23, 2026, 11:43 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a123c4f67388190a885b5ce89f9baa6 |
completed | May 23, 2026, 11:46 p.m. |
Created at: April 27, 2026, 7:14 a.m.