Triple
T1383057
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emmy Noether Lecture |
E29381
|
entity |
| Predicate | hasNotableLecturer |
P21690
|
FINISHED |
| Object |
Fan Chung
Fan Chung is a prominent mathematician known for her influential work in graph theory, combinatorics, and spectral graph theory.
|
E157409
|
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: Fan Chung | Statement: [Emmy Noether Lecture, hasNotableLecturer, Fan Chung]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fan Chung Context triple: [Emmy Noether Lecture, hasNotableLecturer, Fan Chung]
-
A.
Vivian Chan
Vivian Chan is a personal name shared by multiple individuals, including professionals in fields such as science, media, and business.
-
B.
Anita Chan
Anita Chan is a prominent scholar known for her influential research on Chinese labor issues and labor rights.
-
C.
Eileen Loo
Eileen Loo was the wife of renowned Chinese-American architect I. M. Pei and a supportive partner throughout his celebrated career.
-
D.
Vicky Chun
Vicky Chun is a collegiate sports administrator best known for serving as the director of athletics at Yale University.
-
E.
Gwen May-Wan Kao
Gwen May-Wan Kao is best known as the wife and long-time partner of Nobel Prize–winning physicist Charles K. Kao, often recognized for supporting his pioneering work in fiber-optic communications.
- 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: Fan Chung Triple: [Emmy Noether Lecture, hasNotableLecturer, Fan Chung]
Generated description
Fan Chung is a prominent mathematician known for her influential work in graph theory, combinatorics, and spectral graph theory.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fan Chung Target entity description: Fan Chung is a prominent mathematician known for her influential work in graph theory, combinatorics, and spectral graph theory.
-
A.
Vivian Chan
Vivian Chan is a personal name shared by multiple individuals, including professionals in fields such as science, media, and business.
-
B.
Anita Chan
Anita Chan is a prominent scholar known for her influential research on Chinese labor issues and labor rights.
-
C.
Eileen Loo
Eileen Loo was the wife of renowned Chinese-American architect I. M. Pei and a supportive partner throughout his celebrated career.
-
D.
Vicky Chun
Vicky Chun is a collegiate sports administrator best known for serving as the director of athletics at Yale University.
-
E.
Gwen May-Wan Kao
Gwen May-Wan Kao is best known as the wife and long-time partner of Nobel Prize–winning physicist Charles K. Kao, often recognized for supporting his pioneering work in fiber-optic communications.
- 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_69a498d883a48190bfdca525296ef7ee |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c48ff58c8190aeaf09d3e7cad7c7 |
completed | March 1, 2026, 10:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acd48c41f4819092f7e1302d803662 |
completed | March 8, 2026, 1:44 a.m. |
| NEDg | Description generation | batch_69acd543a0ac8190b9fd5e921b5ad9ea |
completed | March 8, 2026, 1:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69acd5b8fa2481908fd52d94e55b6377 |
completed | March 8, 2026, 1:49 a.m. |
Created at: March 1, 2026, 7:59 p.m.