Triple
T146427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | xAI |
E3339
|
entity |
| Predicate | hasTeamMember |
P7184
|
FINISHED |
| Object |
Jimmy Ba
Jimmy Ba is a prominent machine learning researcher known for his work on deep learning optimization methods such as the Adam optimizer.
|
E34729
|
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: Jimmy Ba | Statement: [xAI, hasTeamMember, Jimmy Ba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jimmy Ba Context triple: [xAI, hasTeamMember, Jimmy Ba]
-
A.
Jim
Jim is a common English given name, typically used as a diminutive or familiar form of James.
-
B.
Bobby
Bobby is a common diminutive or nickname for the given name Robert, often used in English-speaking countries.
-
C.
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.
-
D.
Tony James
Tony James is an American financier and executive best known as the longtime president and chief operating officer of Blackstone and for his leadership roles at major cultural institutions.
-
E.
Snitz Edwards
Snitz Edwards was a Hungarian-American character actor of the silent film era, known for his comic and supporting roles in numerous Hollywood 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: Jimmy Ba Triple: [xAI, hasTeamMember, Jimmy Ba]
Generated description
Jimmy Ba is a prominent machine learning researcher known for his work on deep learning optimization methods such as the Adam optimizer.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jimmy Ba Target entity description: Jimmy Ba is a prominent machine learning researcher known for his work on deep learning optimization methods such as the Adam optimizer.
-
A.
Jim
Jim is a common English given name, typically used as a diminutive or familiar form of James.
-
B.
Bobby
Bobby is a common diminutive or nickname for the given name Robert, often used in English-speaking countries.
-
C.
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.
-
D.
Tony James
Tony James is an American financier and executive best known as the longtime president and chief operating officer of Blackstone and for his leadership roles at major cultural institutions.
-
E.
Snitz Edwards
Snitz Edwards was a Hungarian-American character actor of the silent film era, known for his comic and supporting roles in numerous Hollywood 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_69a252868de4819080e21c9938bfe8b6 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a25bab43608190ba5ebfbee6b5b6e4 |
completed | Feb. 28, 2026, 3:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a389a873d48190ac41ff919027688a |
completed | March 1, 2026, 12:34 a.m. |
| NEDg | Description generation | batch_69a38a1053fc819089599155120c3b83 |
completed | March 1, 2026, 12:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a38a5b53bc8190bf87f9260850fe9b |
completed | March 1, 2026, 12:37 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.