Triple
T9029454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RenVM |
E216131
|
entity |
| Predicate | supports |
P516
|
FINISHED |
| Object |
Polygon
Polygon is a popular Ethereum scaling platform that provides a framework for building and connecting fast, low-cost, and interoperable blockchain networks.
|
E773505
|
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: Polygon | Statement: [RenVM, supports, Polygon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Polygon Context triple: [RenVM, supports, Polygon]
-
A.
Square
Square is a financial technology company best known for its mobile payment solutions and point-of-sale hardware that enable businesses to accept card payments easily.
-
B.
ctangle
ctangle is a CWEB tool that converts literate CWEB source files into compilable C code by extracting and tangling the program fragments.
-
C.
Oktogon
Oktogon is a major octagonal intersection and public square in central Budapest, Hungary, where Andrássy Avenue crosses the Grand Boulevard.
-
D.
Shape
Shape is a health and fitness magazine and digital brand focused on exercise, nutrition, and wellness content, owned by Dotdash Meredith.
-
E.
Polygon Pictures
Polygon Pictures is a Japanese animation studio known for its 3D CGI work on numerous anime series and international co-productions.
- 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: Polygon Triple: [RenVM, supports, Polygon]
Generated description
Polygon is a popular Ethereum scaling platform that provides a framework for building and connecting fast, low-cost, and interoperable blockchain networks.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Polygon Target entity description: Polygon is a popular Ethereum scaling platform that provides a framework for building and connecting fast, low-cost, and interoperable blockchain networks.
-
A.
Square
Square is a financial technology company best known for its mobile payment solutions and point-of-sale hardware that enable businesses to accept card payments easily.
-
B.
ctangle
ctangle is a CWEB tool that converts literate CWEB source files into compilable C code by extracting and tangling the program fragments.
-
C.
Oktogon
Oktogon is a major octagonal intersection and public square in central Budapest, Hungary, where Andrássy Avenue crosses the Grand Boulevard.
-
D.
Shape
Shape is a health and fitness magazine and digital brand focused on exercise, nutrition, and wellness content, owned by Dotdash Meredith.
-
E.
Polygon Pictures
Polygon Pictures is a Japanese animation studio known for its 3D CGI work on numerous anime series and international co-productions.
- 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_69ca83a5fa88819088144801b4dd7245 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6a9bcb508190b58751f1772407d4 |
completed | April 1, 2026, 12:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfdbc289648190834031537c8ce130 |
completed | April 3, 2026, 3:24 p.m. |
| NEDg | Description generation | batch_69cfde57a18c8190b4b8c8d2f521bd2c |
completed | April 3, 2026, 3:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cfdec619d081909fd6b268f4ce06b9 |
completed | April 3, 2026, 3:37 p.m. |
Created at: March 30, 2026, 7:08 p.m.