Triple
T19941913
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Book of Concealment |
E479326
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Zohar |
—
|
NE NERFINISHED |
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: Zohar | Statement: [Book of Concealment, partOf, Zohar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zohar Context triple: [Book of Concealment, partOf, Zohar]
-
A.
Zohar
chosen
The Zohar is the central mystical text of Jewish Kabbalah, presenting esoteric interpretations of the Torah and profound teachings on the nature of God, the cosmos, and the soul.
-
B.
Zohar
Zohar is a fictional character portrayed by Israeli-American actress Alona Tal, known from her work in film and television.
-
C.
Zohar Manna
Zohar Manna was a pioneering computer scientist known for his foundational work in mathematical logic, program verification, and the formal methods of software correctness.
-
D.
Biurei HaZohar
Biurei HaZohar is a multi-volume collection of mystical and Hasidic commentaries on the Zohar authored by the second Chabad Rebbe, Dovber Schneuri.
-
E.
Sefer Yetzirah
Sefer Yetzirah is an early foundational work of Jewish mysticism that explores creation through the Hebrew letters and the ten sefirot.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e522a17c819095165d4d24939fd8 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65a6318848190a1dd3e0a6fea3fe2 |
completed | April 20, 2026, 4:54 p.m. |
Created at: April 10, 2026, 1:54 p.m.