Triple
T7113587
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blue Origin |
E165761
|
entity |
| Predicate | keyPerson |
P256
|
FINISHED |
| Object |
Dave Limp
Dave Limp is a technology executive known for leading Amazon’s devices and services division, including the development of Alexa and Kindle, before becoming CEO of Blue Origin.
|
E642857
|
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: Dave Limp | Statement: [Blue Origin, keyPerson, Dave Limp]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dave Limp Context triple: [Blue Origin, keyPerson, Dave Limp]
-
A.
Dan Jinks
Dan Jinks is an American film and television producer best known for acclaimed movies such as "American Beauty" and "Big Fish."
-
B.
Dave Jellison
Dave Jellison is a musician known for his association with the American glam metal band Ratt.
-
C.
Jeff Timmons
Jeff Timmons is an American singer and founding member of the pop and R&B boy band 98 Degrees.
-
D.
Mike Krieger
Mike Krieger is a Brazilian-American entrepreneur and software engineer best known as the co-founder and former CTO of the photo-sharing social media platform Instagram.
-
E.
Dan Rydell
Dan Rydell is a charismatic, quick-witted sports anchor and one of the central protagonists on the television series "Sports Night."
- 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: Dave Limp Triple: [Blue Origin, keyPerson, Dave Limp]
Generated description
Dave Limp is a technology executive known for leading Amazon’s devices and services division, including the development of Alexa and Kindle, before becoming CEO of Blue Origin.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dave Limp Target entity description: Dave Limp is a technology executive known for leading Amazon’s devices and services division, including the development of Alexa and Kindle, before becoming CEO of Blue Origin.
-
A.
Dan Jinks
Dan Jinks is an American film and television producer best known for acclaimed movies such as "American Beauty" and "Big Fish."
-
B.
Dave Jellison
Dave Jellison is a musician known for his association with the American glam metal band Ratt.
-
C.
Jeff Timmons
Jeff Timmons is an American singer and founding member of the pop and R&B boy band 98 Degrees.
-
D.
Mike Krieger
Mike Krieger is a Brazilian-American entrepreneur and software engineer best known as the co-founder and former CTO of the photo-sharing social media platform Instagram.
-
E.
Dan Rydell
Dan Rydell is a charismatic, quick-witted sports anchor and one of the central protagonists on the television series "Sports Night."
- 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_69c6888120f081908f8f01b201dc4a4c |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e5ef813c8190bec0ab0cbae430e5 |
completed | March 27, 2026, 8:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c79cbc35d48190974e207eb98dcbe3 |
completed | March 28, 2026, 9:17 a.m. |
| NEDg | Description generation | batch_69c79d31a9e8819096e6a3040b1852a9 |
completed | March 28, 2026, 9:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c79dcae54c8190b06e687236373f68 |
completed | March 28, 2026, 9:22 a.m. |
Created at: March 27, 2026, 2:43 p.m.