Triple
T229863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amr Moussa |
E4387
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Moussa |
E30384
|
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: Moussa | Statement: [Amr Moussa, familyName, Moussa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moussa Context triple: [Amr Moussa, familyName, Moussa]
-
A.
Bambara
Bambara is a major Mande language widely spoken in Mali and neighboring West African countries, serving as a key lingua franca in the region.
-
B.
Nasar
Nasar is a surname most notably associated with Sylvia Nasar, the economist and author of "A Beautiful Mind."
-
C.
Olara Otunnu
Olara Otunnu is a Ugandan diplomat, lawyer, and human rights advocate known for his work on behalf of war-affected children and his leadership roles at the United Nations.
-
D.
Amr
chosen
Amr is a common Arabic male given name, often associated with historical and contemporary figures across the Arab world.
-
E.
Monsieur Ibrahim
Monsieur Ibrahim is a 2003 French drama film in which Omar Sharif delivers an acclaimed performance as a wise Turkish shopkeeper who befriends a lonely Parisian boy.
- 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_69a257363ffc81909757bde7ab3404da |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25cac7994819080b0b3b10808f8e5 |
completed | Feb. 28, 2026, 3:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3736e4010819089000ed60dbf519c |
completed | Feb. 28, 2026, 10:59 p.m. |
Created at: Feb. 28, 2026, 2:53 a.m.