Triple

T29725185
Position Surface form Disambiguated ID Type / Status
Subject ספר שמות E752160 entity
Predicate attributedAuthor P2353 FINISHED
Object משה רבנו
משה רבנו הוא המנהיג והנביא המרכזי בתולדות עם ישראל, שהוציא את בני ישראל ממצרים, קיבל את התורה בהר סיני ונחשב לדמות מייסדת ביהדות.
E1885417 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: משה רבנו | Statement: [ספר שמות, attributedAuthor, משה רבנו]
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: משה רבנו
Triple: [ספר שמות, attributedAuthor, משה רבנו]
Generated description
משה רבנו הוא המנהיג והנביא המרכזי בתולדות עם ישראל, שהוציא את בני ישראל ממצרים, קיבל את התורה בהר סיני ונחשב לדמות מייסדת ביהדות.

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_69f0d628c00c8190ab5ee7e423d7ec3c completed April 28, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f672ff16a08190b8840351bef56f55 completed May 2, 2026, 9:56 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26e5e07bf88190a3e3cb2f3996c624 completed June 8, 2026, 3:55 p.m.
NEDg Description generation batch_6a26e65d91c4819084e7d18eed703efc completed June 8, 2026, 3:57 p.m.
NED2 Entity disambiguation (via description) batch_6a26e6c18d1c819087ea82e4718ffb84 completed June 8, 2026, 3:58 p.m.
Created at: April 28, 2026, 7:38 p.m.