Triple
T9463912
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terina |
E228219
|
entity |
| Predicate | hasNameInAncientGreek |
P3659
|
FINISHED |
| Object | Τερίνα |
E228219
|
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: Τερίνα | Statement: [Terina, hasNameInAncientGreek, Τερίνα]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Τερίνα Context triple: [Terina, hasNameInAncientGreek, Τερίνα]
-
A.
Trinetta
Trinetta is a character from the animated television series "Who Asked You?," known for her distinctive personality and role in the show's comedic narrative.
-
B.
Terina
chosen
Terina was an ancient Greek-founded city in southern Italy’s Bruttium region, known as a significant coastal and commercial center in Magna Graecia.
-
C.
Tarusa
Tarusa is a small historic town in western Russia known for its scenic location on the Oka River and its associations with Russian artists and writers.
-
D.
Teri
Teri is a central character in the film and television series "Soul Food," known as the ambitious, high-powered attorney whose strained relationships with her family drive much of the story’s drama.
-
E.
Trisaia
Trisaia is an ENEA research center site in southern Italy known for its activities in energy, environmental, and nuclear technology research.
- 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_69ca846fee388190a6ec273fd644b88b |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7fcec2d88190b93b6e4d881c85c6 |
completed | April 1, 2026, 8:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d122a75aa08190adfe03d9f785f1ff |
completed | April 4, 2026, 2:39 p.m. |
Created at: March 30, 2026, 7:53 p.m.