Triple
T12731788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Lava |
E304255
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | William Lava |
E304255
|
NE FINISHED |
How this triple was built (2 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: William Lava | Statement: [William Lava, name, William Lava]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: William Lava Context triple: [William Lava, name, William Lava]
-
A.
William Lava
chosen
William Lava was an American composer best known for scoring numerous Warner Bros. cartoons, including many Looney Tunes and Merrie Melodies shorts.
-
B.
John Lowin
John Lowin was a prominent early 17th-century English actor associated with Shakespeare’s company, known for performing major roles in Jacobean and Caroline drama.
-
C.
William Pierson
William Pierson is a tough, battle-hardened U.S. Army staff sergeant and key supporting character in the World War II–themed video game Call of Duty: WWII.
-
D.
Joseph Winters
Joseph Winters is a fictional character from the 2012 ensemble drama-comedy film "Darling Companion," which centers on family relationships and the search for a lost dog.
-
E.
William Froug
William Froug was an American television producer, writer, and educator best known for his work on classic series such as The Twilight Zone.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d7bdf1426c8190a4402e1c4cdec33a |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96467a2248190aff1ebb5db84b3c6 |
completed | April 10, 2026, 8:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6af49d2c4819097168712af7d4c15 |
completed | May 3, 2026, 2:13 a.m. |
Created at: April 9, 2026, 5:25 p.m.