Triple
T4485928
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fullerian Professor of Chemistry |
E107237
|
entity |
| Predicate | hasNotableHolder |
P1918
|
FINISHED |
| Object |
Mimi Hii
Mimi Hii is a prominent chemist known for her research in catalysis and sustainable chemistry, holding a prestigious professorship at Imperial College London.
|
E446192
|
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: Mimi Hii | Statement: [Fullerian Professor of Chemistry, hasNotableHolder, Mimi Hii]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mimi Hii Context triple: [Fullerian Professor of Chemistry, hasNotableHolder, Mimi Hii]
-
A.
Mimi
Mimi is a common affectionate diminutive or nickname for the given name Marie.
-
B.
Miki
Miki is a city in Japan located within Hyogo Prefecture, known for its traditional hardware industry and historical sites.
-
C.
Rei Momo
Rei Momo is David Byrne’s debut solo studio album, known for its vibrant fusion of Latin music styles with art rock sensibilities.
-
D.
Lolei
Lolei is an ancient temple in Cambodia’s Angkor region, known as one of the Roluos Group of early Khmer brick towers built during the late 9th century.
-
E.
Miya
Miya is a Chadic language spoken in parts of northern Nigeria, known for its complex tonal system and Afroasiatic linguistic roots.
- 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: Mimi Hii Triple: [Fullerian Professor of Chemistry, hasNotableHolder, Mimi Hii]
Generated description
Mimi Hii is a prominent chemist known for her research in catalysis and sustainable chemistry, holding a prestigious professorship at Imperial College London.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mimi Hii Target entity description: Mimi Hii is a prominent chemist known for her research in catalysis and sustainable chemistry, holding a prestigious professorship at Imperial College London.
-
A.
Mimi
Mimi is a common affectionate diminutive or nickname for the given name Marie.
-
B.
Miki
Miki is a city in Japan located within Hyogo Prefecture, known for its traditional hardware industry and historical sites.
-
C.
Rei Momo
Rei Momo is David Byrne’s debut solo studio album, known for its vibrant fusion of Latin music styles with art rock sensibilities.
-
D.
Lolei
Lolei is an ancient temple in Cambodia’s Angkor region, known as one of the Roluos Group of early Khmer brick towers built during the late 9th century.
-
E.
Miya
Miya is a Chadic language spoken in parts of northern Nigeria, known for its complex tonal system and Afroasiatic linguistic roots.
- 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_69bd43f84f788190a1383579c4a595be |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd52a958288190974b292f54a0e045 |
completed | March 20, 2026, 1:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd679cd3b88190a9b90f50f2b7beae |
completed | March 20, 2026, 3:28 p.m. |
| NEDg | Description generation | batch_69bd683417b08190bc4e08638a30c0ec |
completed | March 20, 2026, 3:31 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bd68b4681c8190abb170ccb054cd05 |
completed | March 20, 2026, 3:33 p.m. |
Created at: March 20, 2026, 12:59 p.m.