Triple
T2706897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Foxconn |
E59361
|
entity |
| Predicate | chairman |
P377
|
FINISHED |
| Object |
Young Liu
Young Liu is a Taiwanese business executive best known as the chairman of Foxconn, the world’s largest electronics manufacturing services provider.
|
E290109
|
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: Young Liu | Statement: [Foxconn, chairman, Young Liu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Young Liu Context triple: [Foxconn, chairman, Young Liu]
-
A.
Yao Chen
Yao Chen is a prominent Chinese actress and philanthropist known for her influential social media presence and advocacy on social issues.
-
B.
Younan Xia
Younan Xia is a prominent chemist and materials scientist known for his pioneering work in nanomaterials synthesis and nanotechnology.
-
C.
Jeff Wu
Jeff Wu is a machine learning researcher known for his work on large language models, including co-authoring the original GPT-2 paper at OpenAI.
-
D.
Jun Xia
Jun Xia is a Chinese architect best known for serving as the lead designer of Shanghai Tower, one of the world’s tallest skyscrapers.
-
E.
Wei Liu
Wei Liu is a computer scientist and researcher known for his contributions to deep learning and computer vision, including influential work on object detection.
- 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: Young Liu Triple: [Foxconn, chairman, Young Liu]
Generated description
Young Liu is a Taiwanese business executive best known as the chairman of Foxconn, the world’s largest electronics manufacturing services provider.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Young Liu Target entity description: Young Liu is a Taiwanese business executive best known as the chairman of Foxconn, the world’s largest electronics manufacturing services provider.
-
A.
Yao Chen
Yao Chen is a prominent Chinese actress and philanthropist known for her influential social media presence and advocacy on social issues.
-
B.
Younan Xia
Younan Xia is a prominent chemist and materials scientist known for his pioneering work in nanomaterials synthesis and nanotechnology.
-
C.
Jeff Wu
Jeff Wu is a machine learning researcher known for his work on large language models, including co-authoring the original GPT-2 paper at OpenAI.
-
D.
Jun Xia
Jun Xia is a Chinese architect best known for serving as the lead designer of Shanghai Tower, one of the world’s tallest skyscrapers.
-
E.
Wei Liu
Wei Liu is a computer scientist and researcher known for his contributions to deep learning and computer vision, including influential work on object detection.
- 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_69ab4ac66bc88190b9e4afa5fc843f72 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abda725f24819090e8d936b3d2d5bc |
completed | March 7, 2026, 7:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afaf7c22a4819096ff9effe9e0d77d |
completed | March 10, 2026, 5:43 a.m. |
| NEDg | Description generation | batch_69afb039cdcc8190953368c1f6757503 |
completed | March 10, 2026, 5:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afb0c27ee48190a319fd2fab02f755 |
completed | March 10, 2026, 5:48 a.m. |
Created at: March 6, 2026, 9:55 p.m.