Triple
T10265269
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wallace Fard Muhammad |
E240695
|
entity |
| Predicate | taughtConcept |
P62557
|
FINISHED |
| Object | separation from white society |
—
|
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: separation from white society | Statement: [Wallace Fard Muhammad, taughtConcept, separation from white society]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: taughtConcept Context triple: [Wallace Fard Muhammad, taughtConcept, separation from white society]
-
A.
trainingConcept
chosen
Indicates that one entity serves as a concept, topic, or subject matter that is being taught or trained on in relation to another entity.
-
B.
introducedConcept
Indicates that one entity is responsible for presenting, defining, or bringing a new concept into use or awareness for another entity or context.
-
C.
taughtThat
Indicates that one entity provided instruction or education to another entity about a specific subject, skill, or concept.
-
D.
demonstratedConcept
Indicates that an entity has shown, illustrated, or made evident a particular concept through example, explanation, or action.
-
E.
teachesAbout
Indicates that one entity provides instruction or information to another entity on a particular subject or topic.
- 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_69d381a94c1881908fc38fc263d9b9c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2872830819080fdfa816167d04c |
completed | April 7, 2026, 9:46 a.m. |
| PD | Predicate disambiguation | batch_69d4d1ef6e6c81908a8ee52e4d28127b |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:33 a.m.