Triple
T25955171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yalan Dünya |
E654073
|
entity |
| Predicate | hasTitleMeaning |
P4542
|
FINISHED |
| Object |
"Fake World" in English
"Fake World" is the English title of the Turkish television series "Yalan Dünya," a popular comedy-drama about the intertwined lives of eccentric residents in an Istanbul neighborhood.
|
E1703722
|
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: "Fake World" in English | Statement: [Yalan Dünya, hasTitleMeaning, "Fake World" in English]
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: "Fake World" in English Triple: [Yalan Dünya, hasTitleMeaning, "Fake World" in English]
Generated description
"Fake World" is the English title of the Turkish television series "Yalan Dünya," a popular comedy-drama about the intertwined lives of eccentric residents in an Istanbul neighborhood.
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_69e7ab40ac788190a771bc499eb1ae5f |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f6049c6bbc8190ac502c85741eefd3 |
completed | May 2, 2026, 2:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a110779fa8c81909f6864e47d928cb3 |
completed | May 23, 2026, 1:48 a.m. |
| NEDg | Description generation | batch_6a1108ea3754819081686ac7fa8f8e1b |
completed | May 23, 2026, 1:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a1109785d80819093c4602d784e1485 |
completed | May 23, 2026, 1:57 a.m. |
Created at: April 22, 2026, 8:44 a.m.