Triple
T3932957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Auston Matthews |
E90837
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Auston
Auston is the given name of Auston Matthews, a prominent American professional ice hockey player in the NHL.
|
E399616
|
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: Auston | Statement: [Auston Matthews, givenName, Auston]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Auston Context triple: [Auston Matthews, givenName, Auston]
-
A.
Ashton
Ashton is a masculine given name of English origin that has become well known through figures such as actor and entrepreneur Ashton Kutcher.
-
B.
Ashton
Ashton is a small village in the town of Cumberland in Providence County, Rhode Island, known for its historic mill district along the Blackstone River.
-
C.
Ashton
Ashton is a small town in South Africa’s Western Cape, known for its fruit farming and position along the scenic Route 62.
-
D.
Addison
Addison is a Chicago Transit Authority 'L' station on the Blue Line serving the city's Northwest Side.
-
E.
Addison
Addison is a small, business-focused town in the Dallas–Fort Worth metropolitan area known for its dense concentration of restaurants, corporate offices, and frequent special events.
- 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: Auston Triple: [Auston Matthews, givenName, Auston]
Generated description
Auston is the given name of Auston Matthews, a prominent American professional ice hockey player in the NHL.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Auston Target entity description: Auston is the given name of Auston Matthews, a prominent American professional ice hockey player in the NHL.
-
A.
Ashton
Ashton is a masculine given name of English origin that has become well known through figures such as actor and entrepreneur Ashton Kutcher.
-
B.
Ashton
Ashton is a small village in the town of Cumberland in Providence County, Rhode Island, known for its historic mill district along the Blackstone River.
-
C.
Ashton
Ashton is a small town in South Africa’s Western Cape, known for its fruit farming and position along the scenic Route 62.
-
D.
Addison
Addison is a Chicago Transit Authority 'L' station on the Blue Line serving the city's Northwest Side.
-
E.
Addison
Addison is a small, business-focused town in the Dallas–Fort Worth metropolitan area known for its dense concentration of restaurants, corporate offices, and frequent special events.
- 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_69aed95f26e0819094b0e71974543a19 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeedaaf3c881909539831bf3a8bf10 |
completed | March 9, 2026, 3:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b52887d4a48190b51df3f51ff197c0 |
completed | March 14, 2026, 9:21 a.m. |
| NEDg | Description generation | batch_69b529a1486881908ff348558199232b |
completed | March 14, 2026, 9:25 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b52a43c6f081908366d9848728f98a |
completed | March 14, 2026, 9:28 a.m. |
Created at: March 9, 2026, 3:23 p.m.