Triple
T17024409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rumble in the Bronx |
E413025
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Keung
Keung is the courageous and resourceful Hong Kong cop who becomes the central hero in the action-comedy film "Rumble in the Bronx."
|
E1245675
|
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: Keung | Statement: [Rumble in the Bronx, mainCharacter, Keung]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Keung Context triple: [Rumble in the Bronx, mainCharacter, Keung]
-
A.
Tai Kwun
Tai Kwun is a major heritage and arts complex in Hong Kong that revitalizes the former Central Police Station compound into a cultural, exhibition, and performance hub.
-
B.
Luk Keng
Luk Keng is a rural area in Hong Kong’s northeastern New Territories known for its scenic wetlands, traditional villages, and hiking trails overlooking Starling Inlet.
-
C.
Kain Kong
Kain Kong is a musician best known as a member of the punk rock band The Lookouts.
-
D.
Kong Lin
Kong Lin is the historic family cemetery of Confucius and his descendants in Qufu, Shandong, and a major Confucian cultural heritage site in China.
-
E.
Kwan
Kwan is a Chinese-origin surname shared by many individuals, including the renowned American figure skater Michelle Kwan.
- 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: Keung Triple: [Rumble in the Bronx, mainCharacter, Keung]
Generated description
Keung is the courageous and resourceful Hong Kong cop who becomes the central hero in the action-comedy film "Rumble in the Bronx."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Keung Target entity description: Keung is the courageous and resourceful Hong Kong cop who becomes the central hero in the action-comedy film "Rumble in the Bronx."
-
A.
Tai Kwun
Tai Kwun is a major heritage and arts complex in Hong Kong that revitalizes the former Central Police Station compound into a cultural, exhibition, and performance hub.
-
B.
Luk Keng
Luk Keng is a rural area in Hong Kong’s northeastern New Territories known for its scenic wetlands, traditional villages, and hiking trails overlooking Starling Inlet.
-
C.
Kain Kong
Kain Kong is a musician best known as a member of the punk rock band The Lookouts.
-
D.
Kong Lin
Kong Lin is the historic family cemetery of Confucius and his descendants in Qufu, Shandong, and a major Confucian cultural heritage site in China.
-
E.
Kwan
Kwan is a Chinese-origin surname shared by many individuals, including the renowned American figure skater Michelle Kwan.
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d5d371148190a60d32a72abec09a |
completed | April 18, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a011b514de481909c78c17a3014b468 |
completed | May 10, 2026, 11:57 p.m. |
| NEDg | Description generation | batch_6a011c021e1c819098e04b1cbaf33ecd |
completed | May 11, 2026, midnight |
| NED2 | Entity disambiguation (via description) | batch_6a011c8afb608190b51c7a4c9ccaa0a5 |
completed | May 11, 2026, 12:02 a.m. |
Created at: April 10, 2026, 5:33 a.m.