Triple
T6200415
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nik |
E138614
|
entity |
| Predicate | teammate |
P2649
|
FINISHED |
| Object | Ato |
E133196
|
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: Ato | Statement: [Nik, teammate, Ato]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ato Context triple: [Nik, teammate, Ato]
-
A.
Ato
chosen
Ato is one of the futuristic, computer-generated "Spheriks" characters who served as an official mascot for the 2002 FIFA World Cup in South Korea and Japan.
-
B.
Ateso
Ateso is a Nilotic language spoken primarily by the Teso people of eastern Uganda and western Kenya.
-
C.
Ateste
Ateste is the ancient name of the Italian town of Este, historically significant as a center of the Venetic civilization in northern Italy.
-
D.
Atoni
Atoni are an indigenous ethnic group of western Timor known for their traditional hierarchical social structure, distinctive architecture, and dryland farming culture.
-
E.
Ate
Ate is a populous district in the eastern part of Lima, Peru, known for its mix of industrial zones, residential areas, and growing commercial activity.
- 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_69c008acbea48190991c6b834bb45d65 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c062547cd48190a2715537b961262e |
completed | March 22, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c16f366cfc81909cca73677268821a |
completed | March 23, 2026, 4:49 p.m. |
Created at: March 22, 2026, 4:20 p.m.