Triple
T10277052
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ottawa Rough Riders |
E240993
|
entity |
| Predicate | notableCoach |
P550
|
FINISHED |
| Object |
Jack Gotta
Jack Gotta was a prominent Canadian Football League coach and executive best known for leading multiple teams to success in the 1970s and 1980s.
|
E851979
|
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: Jack Gotta | Statement: [Ottawa Rough Riders, notableCoach, Jack Gotta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jack Gotta Context triple: [Ottawa Rough Riders, notableCoach, Jack Gotta]
-
A.
Jack
Jack is the standard botanical author abbreviation for William Jack, a 19th-century Scottish physician and botanist known for his work on Southeast Asian flora.
-
B.
Jack
Jack is a common masculine given name, often used as a familiar form of John and widely featured in English-language literature and popular culture.
-
C.
Jimmy Ba
Jimmy Ba is a prominent machine learning researcher known for his work on deep learning optimization methods such as the Adam optimizer.
-
D.
Jake
Jake is a masculine given name commonly used in English-speaking countries, often as a short form of Jacob.
-
E.
Jake
Jake is a fictional character from the "Pacific Rim" film franchise, known as the charismatic Jaeger pilot and son of legendary pilot Stacker Pentecost.
- 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: Jack Gotta Triple: [Ottawa Rough Riders, notableCoach, Jack Gotta]
Generated description
Jack Gotta was a prominent Canadian Football League coach and executive best known for leading multiple teams to success in the 1970s and 1980s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jack Gotta Target entity description: Jack Gotta was a prominent Canadian Football League coach and executive best known for leading multiple teams to success in the 1970s and 1980s.
-
A.
Jack
Jack is a common masculine given name, often used as a familiar form of John and widely featured in English-language literature and popular culture.
-
B.
Jack
Jack is the standard botanical author abbreviation for William Jack, a 19th-century Scottish physician and botanist known for his work on Southeast Asian flora.
-
C.
Jimmy Ba
Jimmy Ba is a prominent machine learning researcher known for his work on deep learning optimization methods such as the Adam optimizer.
-
D.
Jake
Jake is a fictional character from the "Pacific Rim" film franchise, known as the charismatic Jaeger pilot and son of legendary pilot Stacker Pentecost.
-
E.
Jake
Jake is a masculine given name commonly used in English-speaking countries, often as a short form of Jacob.
- 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_69d381a94c1881908fc38fc263d9b9c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d28c3b10819093cdab1392384dd4 |
completed | April 7, 2026, 9:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f82188588190998e06cad1e15e68 |
completed | April 9, 2026, 12:51 a.m. |
| NEDg | Description generation | batch_69d6fcad625881909304201c1ebb3bcb |
completed | April 9, 2026, 1:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d6fd84cd708190816d94417294b52a |
completed | April 9, 2026, 1:14 a.m. |
Created at: April 6, 2026, 11:37 a.m.