Triple
T14841565
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Eythe |
E348976
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Wilson
"Wilson" is a 1944 biographical drama film about U.S. President Woodrow Wilson, noted for its lavish production and multiple Academy Award wins.
|
E204059
|
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: Wilson | Statement: [William Eythe, notableWork, Wilson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wilson Context triple: [William Eythe, notableWork, Wilson]
-
A.
John
John is the given name of Colonel John Quincy, an American military officer and politician after whom John Quincy Adams was named.
-
B.
John
John B. Magruder was a Confederate major general during the American Civil War, known for his leadership in the Peninsula Campaign and his flamboyant personality.
-
C.
John
John was an abbot of Reading Abbey, a senior monastic leader in the medieval English Benedictine community.
-
D.
John
John III, Duke of Brittany, was a 14th-century French nobleman who ruled the Duchy of Brittany and played a key role in the succession disputes that led to the Breton War of Succession.
-
E.
John
John is the given first name of Jack Northrop, the pioneering American aircraft industrialist and designer.
- 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: Wilson Triple: [William Eythe, notableWork, Wilson]
Generated description
"Wilson" is a 1944 biographical drama film about U.S. President Woodrow Wilson, noted for its lavish production and multiple Academy Award wins.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wilson Target entity description: "Wilson" is a 1944 biographical drama film about U.S. President Woodrow Wilson, noted for its lavish production and multiple Academy Award wins.
-
A.
Wilson
chosen
"Wilson" is a 1944 American biographical film about U.S. President Woodrow Wilson, noted for its ambitious production and multiple Academy Awards.
-
B.
Wilson
Wilson is a well-known American sporting goods manufacturer recognized especially for its basketballs and other professional sports equipment.
-
C.
Wilson
Wilson is a masculine given name of English origin commonly used in English-speaking countries.
-
D.
Wilson
Wilson is a common English-language surname borne by numerous notable figures across fields such as science, politics, sports, and the arts.
-
E.
Wilson
Wilson is a city in eastern North Carolina known historically for its tobacco and textile industries and now for its cultural attractions and public gardens.
- F. None of above.
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_69d822ec69008190a9232caa68836872 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded28fa49c81908d1059e6cafd607f |
completed | April 14, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe38a9eb9481908ca509f484007cf6 |
completed | May 8, 2026, 7:25 p.m. |
| NEDg | Description generation | batch_69fe3d0eca948190b107bc593b6e5b72 |
completed | May 8, 2026, 7:44 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe3d94785881908911a7c6f1546d45 |
completed | May 8, 2026, 7:46 p.m. |
Created at: April 10, 2026, 1:53 a.m.