Triple

T19072521
Position Surface form Disambiguated ID Type / Status
Subject Etgar Keret E466825 entity
Predicate familyName P18 FINISHED
Object Keret
Keret is the surname of Etgar Keret, an acclaimed Israeli writer known for his short stories, graphic novels, and screenplays.
 E1357405 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: Keret | Statement: [Etgar Keret, familyName, Keret]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Keret
Context triple: [Etgar Keret, familyName, Keret]
  • A. Katzir
    Katzir is a Hebrew surname notably borne by prominent Israeli scientists and public figures, including former President Ephraim Katzir and biophysicist Aharon Katzir.
  • B. Yakir
    Yakir is a masculine given name of Hebrew origin, commonly used in Israel and among Jewish communities.
  • C. Eyal
    Eyal is a masculine given name of Hebrew origin, commonly used in Israel.
  • D. Savyon
    Savyon is an affluent residential town in central Israel known for its spacious villas, high standard of living, and proximity to Tel Aviv.
  • E. Berko Shemets
    Berko Shemets is a half-Tlingit, half-Jewish police detective in Michael Chabon’s novel *The Yiddish Policemen’s Union*, known for his complex identity and partnership with protagonist Meyer Landsman.
  • 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: Keret
Triple: [Etgar Keret, familyName, Keret]
Generated description
Keret is the surname of Etgar Keret, an acclaimed Israeli writer known for his short stories, graphic novels, and screenplays.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Keret
Target entity description: Keret is the surname of Etgar Keret, an acclaimed Israeli writer known for his short stories, graphic novels, and screenplays.
  • A. Katzir
    Katzir is a Hebrew surname notably borne by prominent Israeli scientists and public figures, including former President Ephraim Katzir and biophysicist Aharon Katzir.
  • B. Yakir
    Yakir is a masculine given name of Hebrew origin, commonly used in Israel and among Jewish communities.
  • C. Eyal
    Eyal is a masculine given name of Hebrew origin, commonly used in Israel.
  • D. Savyon
    Savyon is an affluent residential town in central Israel known for its spacious villas, high standard of living, and proximity to Tel Aviv.
  • E. Berko Shemets
    Berko Shemets is a half-Tlingit, half-Jewish police detective in Michael Chabon’s novel *The Yiddish Policemen’s Union*, known for his complex identity and partnership with protagonist Meyer Landsman.
  • 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_69d8dd04f4488190b1121cc53ef2bfd6 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5e2e1ce5881908367424c89d73feb completed April 20, 2026, 8:25 a.m.
NED1 Entity disambiguation (via context triple) batch_6a05d35ca6848190a6d6695f9156488d completed May 14, 2026, 1:51 p.m.
NEDg Description generation batch_6a05d53fbdf481908f60131dd2fcb15d completed May 14, 2026, 1:59 p.m.
NED2 Entity disambiguation (via description) batch_6a05d5a12d708190922eb39ce2b515f8 completed May 14, 2026, 2:01 p.m.
Created at: April 10, 2026, 12:04 p.m.