Triple
T19637098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Al-Yasa |
E471424
|
entity |
| Predicate | sharesTraditionWith |
P11721
|
FINISHED |
| Object | Musa |
—
|
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: Musa | Statement: [Al-Yasa, sharesTraditionWith, Musa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Musa Context triple: [Al-Yasa, sharesTraditionWith, Musa]
-
A.
Musa
chosen
Musa is the name used in the Quran for the prophet Moses, a central figure in Islamic tradition known for leading the Israelites and receiving divine revelation.
-
B.
Musa
Musa is a central character in Arundhati Roy’s novel "The Ministry of Utmost Happiness," around whom key political and personal conflicts in Kashmir revolve.
-
C.
Musa
Musa is a central character in the documentary film "The Bengal Tiger at the Baghdad Zoo," which follows the experiences of Iraqis and American soldiers amid the chaos of post-invasion Baghdad.
-
D.
Musa
Musa is a South Korean historical epic film starring Jung Woo-sung, known for its large-scale battle scenes and depiction of warriors during the Ming dynasty era.
-
E.
Musa
Musa was a Roman slave who became queen of the Parthian Empire and co-ruled with her son after marrying King Phraates IV.
- 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_69d8e511f28481909f4bc3ea9191e54a |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e64107f3fc8190ace6ae67287d280c |
completed | April 20, 2026, 3:06 p.m. |
Created at: April 10, 2026, 1:44 p.m.