Triple
T20961660
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Musa al-Kadhim |
E516262
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Musa
Musa is a common Arabic male given name of Quranic origin, widely used across the Muslim world and equivalent to the name Moses.
|
E81197
|
NE FINISHED |
How this triple was built (4 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: [Musa al-Kadhim, givenName, Musa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Musa Context triple: [Musa al-Kadhim, givenName, Musa]
-
A.
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.
-
B.
Musa
Musa is a genus of large herbaceous flowering plants that includes the bananas and plantains widely cultivated for their edible fruit.
-
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. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Musa Triple: [Musa al-Kadhim, givenName, Musa]
Generated description
Musa is a common Arabic male given name of Quranic origin, widely used across the Muslim world and equivalent to the name Moses.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Musa Target entity description: Musa is a common Arabic male given name of Quranic origin, widely used across the Muslim world and equivalent to the name Moses.
-
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 feminine given name that has been used by various notable individuals, including the American painter and poet Musa McKim.
-
C.
Musa
Musa is a genus of large herbaceous flowering plants that includes the bananas and plantains widely cultivated for their edible fruit.
-
D.
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.
-
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.
Provenance (5 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_69e0b4fde6c48190af1398e7e734629e |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6fb6fd1d48190ad0ec7eb72f84889 |
completed | April 21, 2026, 4:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a092798800481909d437b58467f8e2b |
completed | May 17, 2026, 2:27 a.m. |
| NEDg | Description generation | batch_6a0928b4be448190bd862c8f971e61d9 |
completed | May 17, 2026, 2:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0929734a5c8190911e54b601118bae |
completed | May 17, 2026, 2:35 a.m. |
Created at: April 16, 2026, 1:31 p.m.