Triple
T12383596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | An Naba |
E295803
|
entity |
| Predicate | precedesSurah |
P30311
|
FINISHED |
| Object | An Nazi'at |
E296970
|
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: An Nazi'at | Statement: [An Naba, precedesSurah, An Nazi'at]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: An Nazi'at Context triple: [An Naba, precedesSurah, An Nazi'at]
-
A.
An-Nazi'at
chosen
An-Nazi'at is the 79th chapter of the Qur'an, known for its vivid depiction of the Day of Resurrection and the fate of past nations.
-
B.
Nasib
Nasib is a given name most notably borne by Nasib Yusifbeyli, an Azerbaijani statesman and political figure of the early 20th century.
-
C.
Nagode
"Nagode" is a popular Afro-pop song by Nigerian singer Yemi Alade, known for its catchy melody and blend of contemporary and African musical elements.
-
D.
Nzaman
Nzaman is a dialect of the Fang language spoken by Fang communities in Central Africa.
-
E.
Nahan
Nahan is a small hill town and municipal council in Himachal Pradesh, India, known for its scenic surroundings, pleasant climate, and role as a regional administrative and commercial center.
- 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_69d6ad9e653c8190b1473c860ee53dae |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d93fbc3f608190b0ee3c4f304a94db |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62ac78c5c81909fb3d63bc6c9cc01 |
completed | May 2, 2026, 4:48 p.m. |
Created at: April 8, 2026, 9:54 p.m.