Triple
T21273367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hwang In-ho |
E524323
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
In-ho
In-ho is a Korean given name commonly used for males and borne by various notable figures in South Korea.
|
E1474892
|
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: In-ho | Statement: [Hwang In-ho, givenName, In-ho]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: In-ho Context triple: [Hwang In-ho, givenName, In-ho]
-
A.
Chung-ho
Chung-ho is the former romanized name of Zhonghe District, a populous urban district in New Taipei City, Taiwan.
-
B.
Jinhae
Jinhae is a coastal district in Changwon, South Korea, best known for its large naval base and famous annual cherry blossom festival.
-
C.
Wi Ha-joon
Wi Ha-joon is a South Korean actor and model best known internationally for his breakout role in the hit Netflix survival drama series "Squid Game."
-
D.
Sang-hyun
Sang-hyun is a central character in the South Korean film "Broker," involved in an illicit baby box scheme that explores themes of family, morality, and redemption.
-
E.
Donghae
Donghae is a coastal city in South Korea known for its port facilities and maritime connections, including international ferry routes.
- 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: In-ho Triple: [Hwang In-ho, givenName, In-ho]
Generated description
In-ho is a Korean given name commonly used for males and borne by various notable figures in South Korea.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: In-ho Target entity description: In-ho is a Korean given name commonly used for males and borne by various notable figures in South Korea.
-
A.
Chung-ho
Chung-ho is the former romanized name of Zhonghe District, a populous urban district in New Taipei City, Taiwan.
-
B.
Jinhae
Jinhae is a coastal district in Changwon, South Korea, best known for its large naval base and famous annual cherry blossom festival.
-
C.
Wi Ha-joon
Wi Ha-joon is a South Korean actor and model best known internationally for his breakout role in the hit Netflix survival drama series "Squid Game."
-
D.
Sang-hyun
Sang-hyun is a central character in the South Korean film "Broker," involved in an illicit baby box scheme that explores themes of family, morality, and redemption.
-
E.
Donghae
Donghae is a coastal city in South Korea known for its port facilities and maritime connections, including international ferry routes.
- 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_69e0b516293c819089458ea2ec85f85e |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e73655717c819092f71ed1920f52b5 |
completed | April 21, 2026, 8:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a099006e7c081908aceb2a0574de81e |
completed | May 17, 2026, 9:53 a.m. |
| NEDg | Description generation | batch_6a0990b9b7c8819094f7db294c2e573d |
completed | May 17, 2026, 9:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0991cdf6408190a590d8907f2fe880 |
completed | May 17, 2026, 10 a.m. |
Created at: April 16, 2026, 4:01 p.m.