Triple
T2833641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mustafa |
E62297
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Moustapha |
E304410
|
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: Moustapha | Statement: [Mustafa, hasVariant, Moustapha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moustapha Context triple: [Mustafa, hasVariant, Moustapha]
-
A.
Mustapha
chosen
Mustapha is a variant transliteration of the Arabic given name Mustafa, commonly used in various languages and cultures.
-
B.
Nabil
Nabil is a common Arabic given name meaning "noble" or "honorable," used across many Arabic-speaking and Muslim-majority cultures.
-
C.
Said Malek
Said Malek is the father of Academy Award–winning actor Rami Malek.
-
D.
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.
-
E.
Mohandessin
Mohandessin is a prominent, upscale district in Giza, Egypt, known for its residential neighborhoods, commercial avenues, and vibrant urban life.
- 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_69ab4c3c39188190955b9c49d98463d8 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdec036dc8190bc2d974f6d82eda9 |
completed | March 7, 2026, 8:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b055d3dbb8819094df5e6751dd96c4 |
completed | March 10, 2026, 5:33 p.m. |
Created at: March 6, 2026, 10:01 p.m.