Triple
T17703156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bescot Stadium |
E441359
|
entity |
| Predicate | hasStand |
P6313
|
FINISHED |
| Object |
Tile Choice Stand
Tile Choice Stand is a spectator stand at Bescot Stadium, home of Walsall Football Club in England.
|
E1283319
|
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: Tile Choice Stand | Statement: [Bescot Stadium, hasStand, Tile Choice Stand]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tile Choice Stand Context triple: [Bescot Stadium, hasStand, Tile Choice Stand]
-
A.
TilePro
TilePro is a family of many-core VLIW processors from Tilera, designed for highly parallel, scalable computing in embedded and networking applications.
-
B.
Kachelotplate
Kachelotplate is a small, uninhabited sandbank island in the Wadden Sea off the coast of East Frisia in northwestern Germany.
-
C.
Tile IR
Tile IR is an intermediate representation used within the PlaidML machine learning compiler to express and optimize tensor computations across diverse hardware backends.
-
D.
Tiler
Tiler is the first name of Tiler Peck, a renowned American ballet dancer and principal with the New York City Ballet.
-
E.
Floor Sample
"Floor Sample" is a memoir by Julia Cameron that chronicles her creative life, struggles with addiction, and spiritual recovery as the author of "The Artist’s Way."
- 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: Tile Choice Stand Triple: [Bescot Stadium, hasStand, Tile Choice Stand]
Generated description
Tile Choice Stand is a spectator stand at Bescot Stadium, home of Walsall Football Club in England.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tile Choice Stand Target entity description: Tile Choice Stand is a spectator stand at Bescot Stadium, home of Walsall Football Club in England.
-
A.
TilePro
TilePro is a family of many-core VLIW processors from Tilera, designed for highly parallel, scalable computing in embedded and networking applications.
-
B.
Kachelotplate
Kachelotplate is a small, uninhabited sandbank island in the Wadden Sea off the coast of East Frisia in northwestern Germany.
-
C.
Tile IR
Tile IR is an intermediate representation used within the PlaidML machine learning compiler to express and optimize tensor computations across diverse hardware backends.
-
D.
Tiler
Tiler is the first name of Tiler Peck, a renowned American ballet dancer and principal with the New York City Ballet.
-
E.
Floor Sample
"Floor Sample" is a memoir by Julia Cameron that chronicles her creative life, struggles with addiction, and spiritual recovery as the author of "The Artist’s Way."
- 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_69d8b9ea20b48190ace88bb46b01e6a9 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4729528b88190bd8a104f6f6d4e69 |
completed | April 19, 2026, 6:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a02301ef8488190a71d06780a4f6b29 |
completed | May 11, 2026, 7:38 p.m. |
| NEDg | Description generation | batch_6a0230bd3388819084da9710cfbef59a |
completed | May 11, 2026, 7:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0231983fb8819080d22d038f97ae5d |
completed | May 11, 2026, 7:44 p.m. |
Created at: April 10, 2026, 10:05 a.m.