Triple
T19591792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deuteronomy 25:17–19 |
E470253
|
entity |
| Predicate | characterizesAmalekAs |
P119796
|
FINISHED |
| Object | not fearing God |
—
|
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: not fearing God | Statement: [Deuteronomy 25:17–19, characterizesAmalekAs, not fearing God]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterizesAmalekAs Context triple: [Deuteronomy 25:17–19, characterizesAmalekAs, not fearing God]
-
A.
offensiveCharacteristic
Indicates that one entity possesses a trait, behavior, or quality that is considered insulting, disrespectful, or likely to cause offense to another entity or group.
-
B.
antagonistAttribute
chosen
Indicates that an entity possesses a characteristic or role specifically associated with being an antagonist in a narrative or conflict.
-
C.
antagonistOf
Indicates a relationship where one entity actively opposes, conflicts with, or serves as an adversary to another.
-
D.
targetsAsRacialEnemy
Indicates that one party identifies and treats another party as an enemy specifically on the basis of their race.
-
E.
portraysAdversary
Indicates that one entity depicts or represents another entity as an opponent, enemy, or rival.
- F. None of above.
Provenance (3 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_69d8e510024481908415c0d616fa6186 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e6405646f0819089436d5517c03047 |
completed | April 20, 2026, 3:03 p.m. |
| PD | Predicate disambiguation | batch_69e514dbdb988190b55931a8138c73e7 |
completed | April 19, 2026, 5:46 p.m. |
Created at: April 10, 2026, 1:43 p.m.