Triple
T7387570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Acer |
E170419
|
entity |
| Predicate | foundedBy |
P104
|
FINISHED |
| Object |
Stan Shih
Stan Shih is a Taiwanese entrepreneur and philanthropist best known as the co-founder and longtime leader of the multinational computer company Acer Inc.
|
E660934
|
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: Stan Shih | Statement: [Acer, foundedBy, Stan Shih]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stan Shih Context triple: [Acer, foundedBy, Stan Shih]
-
A.
Steven Shih Chen
Steven Shih Chen is a Taiwanese-American internet entrepreneur best known as a co-founder of YouTube.
-
B.
Albert Tsai
Albert Tsai is an American child actor known for his comedic television roles, including his breakout performance on the sitcom "Trophy Wife."
-
C.
Kenneth Hsu
Kenneth Hsu is a Swiss geologist and oceanographer known for his influential work on marine geology and the Messinian salinity crisis.
-
D.
Philip S. Yu
Philip S. Yu is a prominent computer scientist known for his influential contributions to data mining, databases, and big data analytics.
-
E.
Kuo-Chen Huang
Kuo-Chen Huang was a physicist whose work on electron–phonon coupling in solids led to the formulation of the Huang–Rhys factor in solid-state spectroscopy.
- 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: Stan Shih Triple: [Acer, foundedBy, Stan Shih]
Generated description
Stan Shih is a Taiwanese entrepreneur and philanthropist best known as the co-founder and longtime leader of the multinational computer company Acer Inc.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stan Shih Target entity description: Stan Shih is a Taiwanese entrepreneur and philanthropist best known as the co-founder and longtime leader of the multinational computer company Acer Inc.
-
A.
Steven Shih Chen
Steven Shih Chen is a Taiwanese-American internet entrepreneur best known as a co-founder of YouTube.
-
B.
Albert Tsai
Albert Tsai is an American child actor known for his comedic television roles, including his breakout performance on the sitcom "Trophy Wife."
-
C.
Kenneth Hsu
Kenneth Hsu is a Swiss geologist and oceanographer known for his influential work on marine geology and the Messinian salinity crisis.
-
D.
Philip S. Yu
Philip S. Yu is a prominent computer scientist known for his influential contributions to data mining, databases, and big data analytics.
-
E.
Kuo-Chen Huang
Kuo-Chen Huang was a physicist whose work on electron–phonon coupling in solids led to the formulation of the Huang–Rhys factor in solid-state spectroscopy.
- 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_69c68a5e2c9081909e713ce866e0060a |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f1f2bac481908ac74069182a4ce4 |
completed | March 27, 2026, 9:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c802e56fb48190976612d2a94d6ee5 |
completed | March 28, 2026, 4:33 p.m. |
| NEDg | Description generation | batch_69c803707cec8190bb474c959ef93d48 |
completed | March 28, 2026, 4:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c803ed9ec4819090a9481954060769 |
completed | March 28, 2026, 4:38 p.m. |
Created at: March 27, 2026, 3:08 p.m.