Triple
T14878794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Data |
E349938
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object |
Richard Wang
Richard Wang is a relatively common personal name that may refer to multiple individuals across various professional and academic fields.
|
E1126880
|
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: Richard Wang | Statement: [Data, fullName, Richard Wang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Richard Wang Context triple: [Data, fullName, Richard Wang]
-
A.
Jonathan Wang
Jonathan Wang is a film producer best known for his work on the acclaimed, genre-bending movie "Everything Everywhere All at Once."
-
B.
Edward Wang
Edward Wang is an entrepreneur best known as a founder of the virtualization and cloud computing company VMware.
-
C.
William Wang
William Wang is a Taiwanese-American entrepreneur best known as the founder and longtime CEO of the consumer electronics company Vizio.
-
D.
Richard Wong
Richard Wong is a cinematographer and filmmaker known for his work on feature films such as "Snow Flower and the Secret Fan."
-
E.
Waymond Wang
Waymond Wang is a gentle, optimistic husband and father whose unexpected resilience and kindness play a crucial role in the multiverse-spanning story of the film "Everything Everywhere All at Once."
- 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: Richard Wang Triple: [Data, fullName, Richard Wang]
Generated description
Richard Wang is a relatively common personal name that may refer to multiple individuals across various professional and academic fields.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Richard Wang Target entity description: Richard Wang is a relatively common personal name that may refer to multiple individuals across various professional and academic fields.
-
A.
Jonathan Wang
Jonathan Wang is a film producer best known for his work on the acclaimed, genre-bending movie "Everything Everywhere All at Once."
-
B.
Edward Wang
Edward Wang is an entrepreneur best known as a founder of the virtualization and cloud computing company VMware.
-
C.
William Wang
William Wang is a Taiwanese-American entrepreneur best known as the founder and longtime CEO of the consumer electronics company Vizio.
-
D.
Richard Wong
Richard Wong is a cinematographer and filmmaker known for his work on feature films such as "Snow Flower and the Secret Fan."
-
E.
Waymond Wang
Waymond Wang is a gentle, optimistic husband and father whose unexpected resilience and kindness play a crucial role in the multiverse-spanning story of the film "Everything Everywhere All at Once."
- 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_69d822ee4f408190b6ac3b2fa434f0df |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5e622388190b2bf91cd10b9821d |
completed | April 15, 2026, 12:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe72ac9f6481908f7b4f63a11fe16c |
completed | May 8, 2026, 11:33 p.m. |
| NEDg | Description generation | batch_69fe74c098548190bf97cfae53868671 |
completed | May 8, 2026, 11:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe75759f1081909c3bb3b642cdc0c2 |
completed | May 8, 2026, 11:44 p.m. |
Created at: April 10, 2026, 1:55 a.m.