Triple
T21431872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maddie Buckley |
E528706
|
entity |
| Predicate | parentOf |
P120
|
FINISHED |
| Object |
Jee-Yun Lee
Jee-Yun Lee is the young daughter of firefighter Maddie Buckley in the television series "9-1-1."
|
E1485249
|
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: Jee-Yun Lee | Statement: [Maddie Buckley, parentOf, Jee-Yun Lee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jee-Yun Lee Context triple: [Maddie Buckley, parentOf, Jee-Yun Lee]
-
A.
SangYup Lee
SangYup Lee is a prominent South Korean automobile designer known for leading Hyundai’s global design direction, including acclaimed models like the Ioniq 5.
-
B.
Sung-Hi Lee
Sung-Hi Lee is a Korean-born American model and actress known for her work in magazines, films, and television.
-
C.
Jae-on Kim
Jae-on Kim is a political scientist known for his work on democratic participation and political equality.
-
D.
Yunsup Lee
Yunsup Lee is a computer architect and entrepreneur best known as a co-creator of the RISC‑V instruction set architecture and a co-founder of the RISC‑V chip company SiFive.
-
E.
Kyunghyun Cho
Kyunghyun Cho is a computer scientist and professor known for his influential work in deep learning and neural machine translation, including early contributions to encoder–decoder architectures and attention mechanisms.
- 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: Jee-Yun Lee Triple: [Maddie Buckley, parentOf, Jee-Yun Lee]
Generated description
Jee-Yun Lee is the young daughter of firefighter Maddie Buckley in the television series "9-1-1."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jee-Yun Lee Target entity description: Jee-Yun Lee is the young daughter of firefighter Maddie Buckley in the television series "9-1-1."
-
A.
SangYup Lee
SangYup Lee is a prominent South Korean automobile designer known for leading Hyundai’s global design direction, including acclaimed models like the Ioniq 5.
-
B.
Sung-Hi Lee
Sung-Hi Lee is a Korean-born American model and actress known for her work in magazines, films, and television.
-
C.
Jae-on Kim
Jae-on Kim is a political scientist known for his work on democratic participation and political equality.
-
D.
Yunsup Lee
Yunsup Lee is a computer architect and entrepreneur best known as a co-creator of the RISC‑V instruction set architecture and a co-founder of the RISC‑V chip company SiFive.
-
E.
Kyunghyun Cho
Kyunghyun Cho is a computer scientist and professor known for his influential work in deep learning and neural machine translation, including early contributions to encoder–decoder architectures and attention mechanisms.
- 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_69e0c455f3688190810bc96365791b0f |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b3ec70f08190b84c4f747cfb290f |
completed | April 22, 2026, 11:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09c8f97f7481909d4b7fc1bc43f0f1 |
completed | May 17, 2026, 1:56 p.m. |
| NEDg | Description generation | batch_6a09c9a6b84881908f9193b89ded7361 |
completed | May 17, 2026, 1:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09caa391588190808eb3dcaff07803 |
completed | May 17, 2026, 2:03 p.m. |
Created at: April 16, 2026, 5:49 p.m.