Triple
T10391107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tetris |
E244892
|
entity |
| Predicate | influenced |
P9
|
FINISHED |
| Object |
Panel de Pon
Panel de Pon is a tile-matching puzzle video game by Nintendo, best known internationally through its rebranded versions like Tetris Attack and Puzzle League.
|
E858955
|
NE FINISHED |
How this triple was built (4 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: Panel de Pon | Statement: [Tetris, influenced, Panel de Pon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Panel de Pon Context triple: [Tetris, influenced, Panel de Pon]
-
A.
Opañel
Opañel is a Madrid Metro station serving the Carabanchel district in Spain.
-
B.
Pengo
Pengo is a Dravidian language spoken primarily by the Pengo people in parts of central India, especially in Odisha and neighboring regions.
-
C.
Puán
Puán is a station on Buenos Aires’ historic Line A subway, serving the Caballito neighborhood near the University of Buenos Aires’ Philosophy and Letters faculty.
-
D.
Putaendo
Putaendo is a small Chilean city in the Valparaíso Region, known for its rural character, historical heritage, and location in the Aconcagua Valley.
-
E.
The Peg
The Peg is a colloquial nickname for Winnipeg, the capital and largest city of the Canadian province of Manitoba.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Panel de Pon Triple: [Tetris, influenced, Panel de Pon]
Generated description
Panel de Pon is a tile-matching puzzle video game by Nintendo, best known internationally through its rebranded versions like Tetris Attack and Puzzle League.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Panel de Pon Target entity description: Panel de Pon is a tile-matching puzzle video game by Nintendo, best known internationally through its rebranded versions like Tetris Attack and Puzzle League.
-
A.
Opañel
Opañel is a Madrid Metro station serving the Carabanchel district in Spain.
-
B.
Pengo
Pengo is a Dravidian language spoken primarily by the Pengo people in parts of central India, especially in Odisha and neighboring regions.
-
C.
Puán
Puán is a station on Buenos Aires’ historic Line A subway, serving the Caballito neighborhood near the University of Buenos Aires’ Philosophy and Letters faculty.
-
D.
Putaendo
Putaendo is a small Chilean city in the Valparaíso Region, known for its rural character, historical heritage, and location in the Aconcagua Valley.
-
E.
The Peg
The Peg is a colloquial nickname for Winnipeg, the capital and largest city of the Canadian province of Manitoba.
- F. None of above. chosen
Provenance (5 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_69d381b5116081908d85227bab6d3c0c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9b5b43081908641a5abfb08dc2b |
completed | April 7, 2026, 11:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d795b9974c819087340adc3622279e |
completed | April 9, 2026, 12:04 p.m. |
| NEDg | Description generation | batch_69d7985e7fc081909fd1ba1dc6f7338c |
completed | April 9, 2026, 12:15 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d799917ab881909a947ad8059652c6 |
completed | April 9, 2026, 12:20 p.m. |
Created at: April 6, 2026, 12:06 p.m.