Triple
T17745091
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tegera Arena |
E442966
|
entity |
| Predicate | sponsor |
P67
|
FINISHED |
| Object | Tegera |
—
|
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: Tegera | Statement: [Tegera Arena, sponsor, Tegera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tegera Context triple: [Tegera Arena, sponsor, Tegera]
-
A.
Tegera
chosen
Tegera is a Swedish brand of protective work gloves and safety equipment produced by the company Ejendals.
-
B.
Tergu
Tergu is a small municipality in the Gallura region of northern Sardinia, Italy, known for its rural setting and historic Romanesque church of Nostra Signora di Tergu.
-
C.
Turitea
Turitea is a rural locality near Palmerston North in New Zealand, known for its water supply facilities and wind farm development.
-
D.
Tatanga
Tatanga is a recurring alien villain in the Super Mario series, best known as the main antagonist of Super Mario Land and nemesis of Princess Daisy.
-
E.
Taquara
Taquara is a residential neighborhood in the West Zone of Rio de Janeiro, Brazil, known for its mix of urban development and remaining green areas.
- 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_69d8b9ed3a2081909b2ec0d4dd2f4c37 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47ad0c5b481909059bfa868cc4001 |
completed | April 19, 2026, 6:48 a.m. |
Created at: April 10, 2026, 10:09 a.m.