Triple
T23138729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seoul World Cup Park |
E577396
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Haneul Park
Haneul Park is a popular eco-friendly hilltop park in Seoul known for its expansive grasslands, sky views, and panoramic city and Han River scenery.
|
E1574953
|
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: Haneul Park | Statement: [Seoul World Cup Park, hasPart, Haneul Park]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haneul Park Context triple: [Seoul World Cup Park, hasPart, Haneul Park]
-
A.
Sunmin Park
Sunmin Park is a film producer best known for her work on the psychological horror movie "The Others."
-
B.
Kihong Park
Kihong Park is a computer scientist known for his research in network traffic modeling, Internet measurement, and performance analysis.
-
C.
Wookyung Jung
Wookyung Jung is a film producer best known for working on the animated feature "The Nut Job."
-
D.
Jae-on Kim
Jae-on Kim is a political scientist known for his work on democratic participation and political equality.
-
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: Haneul Park Triple: [Seoul World Cup Park, hasPart, Haneul Park]
Generated description
Haneul Park is a popular eco-friendly hilltop park in Seoul known for its expansive grasslands, sky views, and panoramic city and Han River scenery.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Haneul Park Target entity description: Haneul Park is a popular eco-friendly hilltop park in Seoul known for its expansive grasslands, sky views, and panoramic city and Han River scenery.
-
A.
Sunmin Park
Sunmin Park is a film producer best known for her work on the psychological horror movie "The Others."
-
B.
Kihong Park
Kihong Park is a computer scientist known for his research in network traffic modeling, Internet measurement, and performance analysis.
-
C.
Wookyung Jung
Wookyung Jung is a film producer best known for working on the animated feature "The Nut Job."
-
D.
Jae-on Kim
Jae-on Kim is a political scientist known for his work on democratic participation and political equality.
-
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_69e245f8e6248190ba3d58e068b4dccb |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e8e23988190814524f63a7efe36 |
completed | April 29, 2026, 4:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c308bbfb481909bb609337bc510e3 |
completed | May 19, 2026, 9:42 a.m. |
| NEDg | Description generation | batch_6a0c34d070ec81908d5f6b7583696c02 |
completed | May 19, 2026, 10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c3551922481908520bf706ad8d7eb |
completed | May 19, 2026, 10:02 a.m. |
Created at: April 17, 2026, 4 p.m.