Triple
T1524324
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ma |
E32300
|
entity |
| Predicate | notableBearer |
P458
|
FINISHED |
| Object |
Ma Kai
Ma Kai is a Chinese politician who served as a Vice Premier of the State Council and played a key role in the country’s economic and financial policymaking.
|
E175090
|
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: Ma Kai | Statement: [Ma, notableBearer, Ma Kai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ma Kai Context triple: [Ma, notableBearer, Ma Kai]
-
A.
Shiso
Shiso is a small inland city in Japan’s Hyogo Prefecture known for its mountainous scenery, forests, and outdoor recreation.
-
B.
Zestaponi
Zestaponi is an industrial town in western Georgia known historically for its manganese processing and role as a regional transport hub.
-
C.
Maki
Maki is a Japanese surname most notably associated with Fumihiko Maki, a prominent modernist architect known for his innovative urban and architectural designs.
-
D.
Tokyo Chiken
Tokyo Chiken is the commonly used abbreviated name for the Tokyo District Public Prosecutors Office, a key prosecutorial authority in Japan’s capital.
-
E.
Ogakumonjo
Ogakumonjo is a historic structure within Kyoto Imperial Palace, traditionally associated with imperial academic or archival functions in Japan’s former capital.
- 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: Ma Kai Triple: [Ma, notableBearer, Ma Kai]
Generated description
Ma Kai is a Chinese politician who served as a Vice Premier of the State Council and played a key role in the country’s economic and financial policymaking.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ma Kai Target entity description: Ma Kai is a Chinese politician who served as a Vice Premier of the State Council and played a key role in the country’s economic and financial policymaking.
-
A.
Shiso
Shiso is a small inland city in Japan’s Hyogo Prefecture known for its mountainous scenery, forests, and outdoor recreation.
-
B.
Zestaponi
Zestaponi is an industrial town in western Georgia known historically for its manganese processing and role as a regional transport hub.
-
C.
Maki
Maki is a Japanese surname most notably associated with Fumihiko Maki, a prominent modernist architect known for his innovative urban and architectural designs.
-
D.
Tokyo Chiken
Tokyo Chiken is the commonly used abbreviated name for the Tokyo District Public Prosecutors Office, a key prosecutorial authority in Japan’s capital.
-
E.
Ogakumonjo
Ogakumonjo is a historic structure within Kyoto Imperial Palace, traditionally associated with imperial academic or archival functions in Japan’s former capital.
- 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_69a885e9b0ac819093a9806ad0efc82c |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9080175588190bb3b1d4b17966f2f |
completed | March 5, 2026, 4:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad2951c1ec8190b7ac04cd820a2bfa |
completed | March 8, 2026, 7:46 a.m. |
| NEDg | Description generation | batch_69ad2d3e2b388190a73f2604d97bb4aa |
completed | March 8, 2026, 8:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad2df61a70819094148563f924a5c5 |
completed | March 8, 2026, 8:06 a.m. |
Created at: March 4, 2026, 7:26 p.m.