Triple
T511227
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Purim |
E10611
|
entity |
| Predicate | commandmentHebrewName |
P14238
|
FINISHED |
| Object | matanot la’evyonim |
—
|
LITERAL 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: matanot la’evyonim | Statement: [Purim, commandmentHebrewName, matanot la’evyonim]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commandmentHebrewName Context triple: [Purim, commandmentHebrewName, matanot la’evyonim]
-
A.
commandmentType
Indicates the specific category or kind of commandment that an instruction or directive belongs to.
-
B.
numberOfCommandments
Indicates the total count of commandments associated with a given subject.
-
C.
biblicalName
Indicates that one entity is the name of a person, place, or concept as it appears in the Bible.
-
D.
scripturalLawCode
Indicates that one entity is a law code or set of legal prescriptions as defined or authorized by a particular scriptural or religious text for another entity.
-
E.
hasNameInHebrew
Indicates that an entity is associated with a specific name expressed in the Hebrew language.
- F. None of above. chosen
Provenance (4 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_69a2e84a0d08819087e01863fcd9abf1 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f165b91c81908c2d2ba15c64b956 |
completed | Feb. 28, 2026, 1:45 p.m. |
| PD | Predicate disambiguation | batch_69a2edfe236481909901cc7d4281b33c |
completed | Feb. 28, 2026, 1:30 p.m. |
| PDg | Predicate description generation | batch_69a2eebc89e8819081fd20beb80fe0ee |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.