Triple
T9112591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Una Stubbs |
E218637
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Una |
E258319
|
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: Una | Statement: [Una Stubbs, givenName, Una]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Una Context triple: [Una Stubbs, givenName, Una]
-
A.
Una
chosen
Una is a feminine given name of Latin origin meaning "one" or "unity," used in various cultures and literary works.
-
B.
Una
Una is a river in the Western Balkans, known for its clear turquoise waters and scenic canyons along the border of Bosnia and Herzegovina and Croatia.
-
C.
Une
Une is a municipality located in Oriente Province, known as one of the local administrative divisions within this eastern region.
-
D.
Ana
Ana is a constituent part of the larger municipality of Santa Ana.
-
E.
Ana
Ana is the given name of Ana Ivanovic, a retired Serbian professional tennis player and former world No. 1.
- 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_69ca83dc94ac8190b9ef42684d36ff39 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca84b0a048190964f560f78e27cce |
completed | April 1, 2026, 5:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d03052716c8190835b0d3357a29ce5 |
completed | April 3, 2026, 9:25 p.m. |
Created at: March 30, 2026, 7:16 p.m.