Triple
T20602462
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hafsa |
E506217
|
entity |
| Predicate | hasVariantSpelling |
P457
|
FINISHED |
| Object | Hafsa(h) |
—
|
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: Hafsa(h) | Statement: [Hafsa, hasVariantSpelling, Hafsa(h)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hafsa(h) Context triple: [Hafsa, hasVariantSpelling, Hafsa(h)]
-
A.
Hafsa
chosen
Hafsa is a feminine given name of Arabic origin, historically borne by notable Ottoman royal figures such as Ayşe Hafsa Sultan.
-
B.
Hafsa Hatun
Hafsa Hatun was an Ottoman-era woman of notable status who is interred in the famed Yeşil Türbe (Green Tomb) in Bursa, Turkey.
-
C.
Habiba
Habiba is a feminine given name commonly used in Arabic-speaking and Muslim-majority cultures, meaning "beloved" or "darling."
-
D.
Aisha
Aisha is a skilled and enigmatic operative who joins the elite black-ops team in the action film "The Losers" (2010).
-
E.
Aisha
Aisha is a female given name of Arabic origin commonly used across the Muslim world and beyond.
- 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_69e0b4ba6ae88190af871e1f9522c704 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6aa20f5c881909265ce7d96efc487 |
completed | April 20, 2026, 10:35 p.m. |
Created at: April 16, 2026, 11:41 a.m.