Triple
T14397571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Three Men and a Little Lady |
E356988
|
entity |
| Predicate | starredActor |
P5563
|
FINISHED |
| Object | Nancy Travis |
—
|
NE NERFINISHED |
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: Nancy Travis | Statement: [Three Men and a Little Lady, starredActor, Nancy Travis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nancy Travis Context triple: [Three Men and a Little Lady, starredActor, Nancy Travis]
-
A.
Nancy Travis
chosen
Nancy Travis is an American actress known for her work in film and television, including prominent roles in series like "The Kominsky Method" and "Last Man Standing."
-
B.
Nancy Mack
Nancy Mack is an American entrepreneur best known as a co-founder of the specialty tea retailer Teavana.
-
C.
Nancy Mack
Nancy Mack is known as the wife of Andrew Mack.
-
D.
Nancy Cummings
Nancy Cummings was the daughter of American poet E. E. Cummings, known primarily through biographical accounts of his personal life.
-
E.
June Travis
June Travis was an American film actress active primarily in the 1930s and 1940s, known for her roles in Hollywood studio productions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d827927c988190ad98bb0360981783 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de90826f908190b3969af9b7cf922f |
completed | April 14, 2026, 7:07 p.m. |
Created at: April 10, 2026, 1:17 a.m.