Triple
T13451849
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nabi |
E320625
|
entity |
| Predicate | appliesToFigure |
P1129
|
FINISHED |
| Object | Musa |
E81197
|
NE 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: Musa | Statement: [Nabi, appliesToFigure, Musa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Musa Context triple: [Nabi, appliesToFigure, 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 genus of large herbaceous flowering plants that includes the bananas and plantains widely cultivated for their edible fruit.
-
D.
Sa’id
Sa’id is a male given name of Arabic origin, commonly meaning "happy" or "fortunate."
-
E.
Moussa
Moussa is the protagonist of the work "Child of Fortune," around whom the story’s central events and character development revolve.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d80761e6cc8190a90c844589998ecc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaefae85481909e6a59797cbb25e7 |
completed | April 12, 2026, 2:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a83382e88190bfb229f9bab59b17 |
completed | May 3, 2026, 7:55 p.m. |
Created at: April 9, 2026, 9:41 p.m.