Triple
T15486860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Project Blue Book |
E377069
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object |
Jin-ho Hur
Jin-ho Hur is a television producer best known for his executive production work on the historical sci-fi drama series "Project Blue Book."
|
E1160738
|
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: Jin-ho Hur | Statement: [Project Blue Book, executiveProducer, Jin-ho Hur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jin-ho Hur Context triple: [Project Blue Book, executiveProducer, Jin-ho Hur]
-
A.
Ho-seok Jung
Ho-seok Jung is a notable individual recognized for achievements significant enough to be associated with the surname Jung.
-
B.
Yong-taek Jung
Yong-taek Jung is a notable individual recognized for bearing the Korean surname Jung.
-
C.
Eui-Sung Yi
Eui-Sung Yi is a prominent architect and urban designer known for his leadership role at the innovative architecture firm Morphosis.
-
D.
Sung-kyu Jung
Sung-kyu Jung is a notable individual recognized as a prominent bearer of the Korean surname Jung.
-
E.
Tae-sung Jeong
Tae-sung Jeong is a television producer best known for serving as an executive producer on the historical sci-fi drama series "Project Blue Book."
- 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: Jin-ho Hur Triple: [Project Blue Book, executiveProducer, Jin-ho Hur]
Generated description
Jin-ho Hur is a television producer best known for his executive production work on the historical sci-fi drama series "Project Blue Book."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jin-ho Hur Target entity description: Jin-ho Hur is a television producer best known for his executive production work on the historical sci-fi drama series "Project Blue Book."
-
A.
Ho-seok Jung
Ho-seok Jung is a notable individual recognized for achievements significant enough to be associated with the surname Jung.
-
B.
Yong-taek Jung
Yong-taek Jung is a notable individual recognized for bearing the Korean surname Jung.
-
C.
Eui-Sung Yi
Eui-Sung Yi is a prominent architect and urban designer known for his leadership role at the innovative architecture firm Morphosis.
-
D.
Sung-kyu Jung
Sung-kyu Jung is a notable individual recognized as a prominent bearer of the Korean surname Jung.
-
E.
Tae-sung Jeong
Tae-sung Jeong is a television producer best known for serving as an executive producer on the historical sci-fi drama series "Project Blue Book."
- 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_69d85cd21dcc81908646251b1c26ea00 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f8f71a08190a440ff19dcc65312 |
completed | April 16, 2026, 1:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff365b3980819094d3ca0b7766009c |
completed | May 9, 2026, 1:27 p.m. |
| NEDg | Description generation | batch_69ff376dac388190ab3b7e3553d2de29 |
completed | May 9, 2026, 1:32 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff382f1bbc8190810d0d825430f9ea |
completed | May 9, 2026, 1:35 p.m. |
Created at: April 10, 2026, 3:47 a.m.