Triple
T4113251
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Buds |
E90226
|
entity |
| Predicate | shortFor |
P43
|
FINISHED |
| Object |
Maple Buds
Maple Buds is a team or group commonly referred to by the shortened name "The Buds."
|
E413425
|
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: Maple Buds | Statement: [The Buds, shortFor, Maple Buds]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maple Buds Context triple: [The Buds, shortFor, Maple Buds]
-
A.
Maples
Maples is the surname of Marla Maples, an American actress and television personality best known as the second wife of former U.S. President Donald Trump.
-
B.
Maple
Maple is a comprehensive computer algebra system used for symbolic and numeric mathematics, modeling, and technical computing across education and research.
-
C.
Maple GO Station
Maple GO Station is a commuter rail station in Maple, Ontario, serving as a local stop on GO Transit's regional rail network in the Greater Toronto Area.
-
D.
Twin Maples
Twin Maples is a historic mansion in Summit, New Jersey, noted for its early 20th-century Colonial Revival architecture and preservation as a local landmark.
-
E.
Jack Maple
Jack Maple was an influential New York City transit police officer and crime strategist best known for co-developing the CompStat system that transformed modern policing.
- 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: Maple Buds Triple: [The Buds, shortFor, Maple Buds]
Generated description
Maple Buds is a team or group commonly referred to by the shortened name "The Buds."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Maple Buds Target entity description: Maple Buds is a team or group commonly referred to by the shortened name "The Buds."
-
A.
Maples
Maples is the surname of Marla Maples, an American actress and television personality best known as the second wife of former U.S. President Donald Trump.
-
B.
Maple
Maple is a comprehensive computer algebra system used for symbolic and numeric mathematics, modeling, and technical computing across education and research.
-
C.
Maple GO Station
Maple GO Station is a commuter rail station in Maple, Ontario, serving as a local stop on GO Transit's regional rail network in the Greater Toronto Area.
-
D.
Twin Maples
Twin Maples is a historic mansion in Summit, New Jersey, noted for its early 20th-century Colonial Revival architecture and preservation as a local landmark.
-
E.
Jack Maple
Jack Maple was an influential New York City transit police officer and crime strategist best known for co-developing the CompStat system that transformed modern policing.
- 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_69aed95c080881908125e30c5dcdc6f8 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af01e06ce48190b840d931e44095ea |
completed | March 9, 2026, 5:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b56b8adc748190be7b34ff37ae3618 |
completed | March 14, 2026, 2:07 p.m. |
| NEDg | Description generation | batch_69b56ca404d88190aad31ec27229cd40 |
completed | March 14, 2026, 2:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b56d908f3481908ff2c703983e84e3 |
completed | March 14, 2026, 2:15 p.m. |
Created at: March 9, 2026, 3:41 p.m.