Triple
T17641089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Min (Chinese surname) |
E429227
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Min Naiben
Min Naiben is a prominent Chinese physicist known for his influential work in optics and photonics.
|
E1278796
|
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: Min Naiben | Statement: [Min (Chinese surname), hasNotableBearer, Min Naiben]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Min Naiben Context triple: [Min (Chinese surname), hasNotableBearer, Min Naiben]
-
A.
Michele Kang
Michele Kang is a business executive and sports entrepreneur best known for leading ownership of the National Women's Soccer League club Washington Spirit.
-
B.
Jani Minga
Jani Minga was an Albanian patriot, educator, and activist known for his role in the Albanian National Awakening and the country’s independence movement.
-
C.
Sibelle Hu
Sibelle Hu is a Taiwanese actress best known for her roles in 1980s Hong Kong action and comedy films.
-
D.
Benjamin Maisani
Benjamin Maisani is a French-born nightclub owner and businessman best known as the longtime partner of American journalist Anderson Cooper.
-
E.
Armin Mizani
Armin Mizani is an American local government leader and attorney who serves as the mayor of Keller, Texas.
- 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: Min Naiben Triple: [Min (Chinese surname), hasNotableBearer, Min Naiben]
Generated description
Min Naiben is a prominent Chinese physicist known for his influential work in optics and photonics.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Min Naiben Target entity description: Min Naiben is a prominent Chinese physicist known for his influential work in optics and photonics.
-
A.
Michele Kang
Michele Kang is a business executive and sports entrepreneur best known for leading ownership of the National Women's Soccer League club Washington Spirit.
-
B.
Jani Minga
Jani Minga was an Albanian patriot, educator, and activist known for his role in the Albanian National Awakening and the country’s independence movement.
-
C.
Sibelle Hu
Sibelle Hu is a Taiwanese actress best known for her roles in 1980s Hong Kong action and comedy films.
-
D.
Benjamin Maisani
Benjamin Maisani is a French-born nightclub owner and businessman best known as the longtime partner of American journalist Anderson Cooper.
-
E.
Armin Mizani
Armin Mizani is an American local government leader and attorney who serves as the mayor of Keller, Texas.
- 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_69d889e2c2608190b762e76d9b2262f1 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46de50bf481909e938613b38f0202 |
completed | April 19, 2026, 5:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a020aa7be948190bf0ab64a2908d3b1 |
completed | May 11, 2026, 4:58 p.m. |
| NEDg | Description generation | batch_6a020b51c208819083b1634221929628 |
completed | May 11, 2026, 5:01 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a020bd466d881909ffdfe3cf932b980 |
completed | May 11, 2026, 5:03 p.m. |
Created at: April 10, 2026, 6:02 a.m.