Triple
T7714393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Love Field |
E174844
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Beth Grant |
E315748
|
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: Beth Grant | Statement: [Love Field, starring, Beth Grant]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beth Grant Context triple: [Love Field, starring, Beth Grant]
-
A.
Beth Grant
chosen
Beth Grant is an American character actress known for her prolific work in film and television, often portraying strict, eccentric, or morally rigid supporting characters.
-
B.
Patricia Haines
Patricia Haines was a British actress known for her television and film roles in the 1950s and 1960s.
-
C.
Katherine Richardson
Katherine Richardson is a Danish-American oceanographer and climate scientist known for her work on marine ecosystems and planetary boundaries.
-
D.
Mary Franklin
Mary Franklin was one of the daughters of Josiah and Abiah Franklin, making her a sister of American founding father Benjamin Franklin.
-
E.
Sara Shaw
Sara Shaw is a film editor known for her work on the acclaimed coming-of-age drama "The Miseducation of Cameron Post."
- 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_69c6995c463c8190a14458036249d419 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702ca8f048190a6ea27b8cee2f93e |
completed | March 27, 2026, 10:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8acd5e32c8190869834b21aeae8a7 |
completed | March 29, 2026, 4:38 a.m. |
Created at: March 27, 2026, 4:04 p.m.