Triple
T8925829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nanna |
E212536
|
entity |
| Predicate | nameVariant |
P744
|
FINISHED |
| Object | Suen |
E765146
|
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: Suen | Statement: [Nanna, nameVariant, Suen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suen Context triple: [Nanna, nameVariant, Suen]
-
A.
Suen
chosen
Suen is the Sumerian moon god, later known as Sin in Akkadian mythology, associated with the lunar cycle, wisdom, and divination.
-
B.
Suinula
Suinula is a locality in Finland historically noted as a site of significant events during the Finnish Civil War’s White Terror period.
-
C.
Sindo
Sindo is an island and administrative division of Ongjin County in Incheon, South Korea, known for its rural landscape and coastal environment.
-
D.
Sonai
Sonai is a prominent town in Assam, India, known as one of the key urban centers of Cachar district in the Barak Valley region.
-
E.
Sosanya
Sosanya is a surname most notably associated with British actress Nina Sosanya, known for her extensive work in television, film, and theatre.
- 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_69ca839481d48190b42b037e0d0f636c |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc66700fb48190874563e535f20437 |
completed | April 1, 2026, 12:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfba58e9ec81909141c516d05ac790 |
completed | April 3, 2026, 1:02 p.m. |
Created at: March 30, 2026, 6:57 p.m.