Triple
T21815506
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blanga |
E538595
|
entity |
| Predicate | neighboringLanguage |
P16383
|
FINISHED |
| Object | Zabana |
—
|
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: Zabana | Statement: [Blanga, neighboringLanguage, Zabana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zabana Context triple: [Blanga, neighboringLanguage, Zabana]
-
A.
Zabana
chosen
Zabana is an Oceanic language spoken in the Solomon Islands, primarily on Santa Isabel Island.
-
B.
Zarda
Zarda is a landmark U.S. Supreme Court case that held federal law prohibits employment discrimination based on sexual orientation.
-
C.
Muzna
Muzna was the mother of Abd al-Rahman III, the powerful 10th-century Umayyad ruler who became the first Caliph of Córdoba in al-Andalus.
-
D.
Zabban
Zabban is an alternate given name of Abu Amr ibn al-Ala, a prominent early Islamic scholar and one of the canonical readers of the Qur’an.
-
E.
Molazzana
Molazzana is a small municipality in Tuscany, central Italy, known for its scenic location in the Garfagnana area of the Apennine mountains.
- 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_69e0c473f0f8819086c9d1b4a143bd67 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f07cc99bbc8190bf074930f361af7d |
completed | April 28, 2026, 9:24 a.m. |
Created at: April 16, 2026, 6:54 p.m.