Triple
T11969803
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruth Rumsey |
E284886
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Ruth Rumsey |
E284886
|
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: Ruth Rumsey | Statement: [Ruth Rumsey, name, Ruth Rumsey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ruth Rumsey Context triple: [Ruth Rumsey, name, Ruth Rumsey]
-
A.
Ruth Rumsey
chosen
Ruth Rumsey was the wife of William J. Donovan, the famed American soldier, lawyer, and head of the Office of Strategic Services during World War II.
-
B.
Ruth Jamison
Ruth Jamison is a central, compassionate character in the novel and film "Fried Green Tomatoes," known for her deep friendship with Idgie Threadgoode and her role in the story’s themes of love, resilience, and female solidarity.
-
C.
Ruth Robbins
Ruth Robbins is an academic and author known for her work in literary and cultural studies.
-
D.
Ann Rumsey
Ann Rumsey was an early settler and landowner in what became Ann Arbor, Michigan, for whom the city was named.
-
E.
Ruth Barrett
Ruth Barrett is a British composer best known for her evocative scores for film and television dramas.
- 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_69d6ab2eaeb881909f7914758f859413 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9037bee54819085242a3ef3e286f9 |
completed | April 10, 2026, 2:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5bab66488190a465e40f1181506e |
completed | May 8, 2026, 3:42 a.m. |
Created at: April 8, 2026, 9:46 p.m.