Triple
T6569679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Banu Ghatafan |
E155400
|
entity |
| Predicate | laterHistory |
P8830
|
FINISHED |
| Object | many members converted to Islam |
—
|
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: many members converted to Islam | Statement: [Banu Ghatafan, laterHistory, many members converted to Islam]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: laterHistory Context triple: [Banu Ghatafan, laterHistory, many members converted to Islam]
-
A.
historicFor
Indicates that something holds historical significance or importance specifically in relation to another entity.
-
B.
subsequentHistory
chosen
Indicates that one event, state, or record occurs or is recorded after another in time, reflecting its later historical development or outcome.
-
C.
historicallyIn
Indicates that one entity existed, occurred, or was situated within the historical context, period, or jurisdiction associated with another entity.
-
D.
historicalPass
Indicates that an entity has successfully completed or passed a historical event, period, or examination related to history.
-
E.
historical
Indicates that the subject has existed, occurred, or been relevant in the past rather than in the present or future.
- 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_69c688151254819080387f87deab8fa7 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6cc9c6cb0819084fec8e0beb430de |
completed | March 27, 2026, 6:29 p.m. |
| PD | Predicate disambiguation | batch_69c6acf93cb48190b54f5dd6febd34dc |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:53 p.m.