Triple
T7662839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruth First |
E173549
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Ruth |
E2634
|
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 | Statement: [Ruth First, givenName, Ruth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ruth Context triple: [Ruth First, givenName, Ruth]
-
A.
Ruth
chosen
Ruth is the given name of Ruth Bader Ginsburg, the pioneering U.S. Supreme Court Justice and prominent advocate for gender equality and civil rights.
-
B.
Ruth
Ruth is a book of the Hebrew Bible/Old Testament that tells the story of a Moabite woman whose loyalty and faith lead to her becoming an ancestor of King David.
-
C.
Ruth
Ruth is the surname of Babe Ruth, the legendary American baseball player widely regarded as one of the greatest hitters in the sport's history.
-
D.
Ruth
Ruth is a supporting character in the comedy Western film "A Million Ways to Die in the West," known for being a devout Christian prostitute engaged to the protagonist's best friend.
-
E.
Ruth
Ruth is a character in Gilbert and Sullivan's comic opera "The Pirates of Penzance," known as the pirate apprentice Frederic's former nursemaid and a source of much of the opera's humor and confusion.
- 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_69c69955517c819085bc715b96d304d2 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c701a74a2c81909f78ab2de7ce807c |
completed | March 27, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c89b1aaef081908d1d181ea7c28c2f |
completed | March 29, 2026, 3:23 a.m. |
Created at: March 27, 2026, 3:59 p.m.