Triple

T35969401
Position Surface form Disambiguated ID Type / Status
Subject Zevulun Orlev E1040237 entity
Predicate positionHeld P8 FINISHED
Object Minister of Labor and Social Affairs
The Minister of Labor and Social Affairs is an Israeli cabinet position responsible for overseeing employment policy, labor relations, and social welfare services.
E2163429 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: Minister of Labor and Social Affairs | Statement: [Zevulun Orlev, positionHeld, Minister of Labor and Social Affairs]
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: Minister of Labor and Social Affairs
Triple: [Zevulun Orlev, positionHeld, Minister of Labor and Social Affairs]
Generated description
The Minister of Labor and Social Affairs is an Israeli cabinet position responsible for overseeing employment policy, labor relations, and social welfare services.

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_69f76e27758c81909b711cf38a130aaf completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7ac23d1388190bdf9628b294943bd completed May 3, 2026, 8:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38b7104e808190aa351f30e9eb2cb7 completed June 22, 2026, 4:16 a.m.
NEDg Description generation batch_6a38b85bf4908190aecf55c46230322b completed June 22, 2026, 4:21 a.m.
NED2 Entity disambiguation (via description) batch_6a38b8dae4ac8190a020a5e984acef6b completed June 22, 2026, 4:23 a.m.
Created at: May 3, 2026, 4:07 p.m.