Triple
T14887726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sandy Rodgers |
E359671
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | Sandy Rodgers |
E359671
|
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: Sandy Rodgers | Statement: [Sandy Rodgers, fullName, Sandy Rodgers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sandy Rodgers Context triple: [Sandy Rodgers, fullName, Sandy Rodgers]
-
A.
Sandy Rodgers
chosen
Sandy Rodgers is the young African American protagonist of Langston Hughes's novel "Not Without Laughter," whose coming-of-age story explores race, family, and identity in early 20th-century Kansas.
-
B.
Sandy Rogers
Sandy Rogers is one of the adopted children of American singer, actress, and cowgirl icon Dale Evans and her husband Roy Rogers.
-
C.
Sandy Duncan
Sandy Duncan is an American actress, singer, and dancer best known for her work on television, Broadway, and in family-oriented entertainment.
-
D.
Patty Dann
Patty Dann is an American novelist and memoirist best known for writing the coming-of-age novel "Mermaids," which was adapted into a popular 1990 film.
-
E.
Marilyn Vance
Marilyn Vance is an American costume designer known for her influential work on numerous popular films, including iconic 1980s and 1990s movies.
- 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_69d827980cbc8190a0c569ae3940a1d9 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69ded5f5b1c88190815f3585770cb135 |
completed | April 15, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fee5dee8988190b80cb487c12bfc2d |
completed | May 9, 2026, 7:44 a.m. |
Created at: April 10, 2026, 2:08 a.m.