Triple
T22163321
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Najwa Nimri |
E547725
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Nimri |
—
|
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: Nimri | Statement: [Najwa Nimri, familyName, Nimri]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nimri Context triple: [Najwa Nimri, familyName, Nimri]
-
A.
Nimri
chosen
Nimri is a Spanish actress and singer best known for her roles in series like "Money Heist" and "Vis a Vis."
-
B.
Gebelawi
Gebelawi is a central, godlike patriarchal figure in Naguib Mahfouz’s novel "Children of Gebelawi," around whom the allegorical family saga and its themes of authority and rebellion revolve.
-
C.
Guraru
Guraru is a town in the Indian state of Bihar, known as a local settlement within the Gaya region.
-
D.
Jonglei
Jonglei is a large, predominantly rural state in eastern South Sudan known for its diverse ethnic groups, recurring intercommunal conflicts, and extensive wetlands along the White Nile.
-
E.
Njoro
Njoro is a town in Kenya’s Rift Valley region known for its agricultural activities and as the home of Egerton University.
- 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_69e11e3c4c5c81908d336165816b12e0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12a2f2f90819080b5bb73a6052c24 |
completed | April 28, 2026, 9:44 p.m. |
Created at: April 16, 2026, 8:34 p.m.