Triple
T7387572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Acer |
E170419
|
entity |
| Predicate | foundedBy |
P104
|
FINISHED |
| Object |
George Huang
George Huang is a Taiwanese entrepreneur best known as a founder of the multinational computer and electronics company Acer Inc.
|
E662459
|
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: George Huang | Statement: [Acer, foundedBy, George Huang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: George Huang Context triple: [Acer, foundedBy, George Huang]
-
A.
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.
-
B.
Kenneth Hsu
Kenneth Hsu is a Swiss geologist and oceanographer known for his influential work on marine geology and the Messinian salinity crisis.
-
C.
Albert Tsai
Albert Tsai is an American child actor known for his comedic television roles, including his breakout performance on the sitcom "Trophy Wife."
-
D.
Yu-Chi Ho
Yu-Chi Ho is a prominent control theorist and engineer known for his pioneering contributions to optimal control, dynamic systems, and game theory.
-
E.
John Cheng
John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
- 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: George Huang Triple: [Acer, foundedBy, George Huang]
Generated description
George Huang is a Taiwanese entrepreneur best known as a founder of the multinational computer and electronics company Acer Inc.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: George Huang Target entity description: George Huang is a Taiwanese entrepreneur best known as a founder of the multinational computer and electronics company Acer Inc.
-
A.
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.
-
B.
Kenneth Hsu
Kenneth Hsu is a Swiss geologist and oceanographer known for his influential work on marine geology and the Messinian salinity crisis.
-
C.
Albert Tsai
Albert Tsai is an American child actor known for his comedic television roles, including his breakout performance on the sitcom "Trophy Wife."
-
D.
Yu-Chi Ho
Yu-Chi Ho is a prominent control theorist and engineer known for his pioneering contributions to optimal control, dynamic systems, and game theory.
-
E.
John Cheng
John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
- 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_69c810ed3a00819088c6eee3d8b8a7e9 |
completed | March 28, 2026, 5:33 p.m. |
| NEDg | Description generation | batch_69c814f36aac8190b2a7501e6398fe93 |
completed | March 28, 2026, 5:50 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c815a93870819099696c249aedbeec |
completed | March 28, 2026, 5:53 p.m. |
Created at: March 27, 2026, 3:08 p.m.