Triple
T887544
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flight into Egypt |
E19163
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Mary |
E14130
|
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: Mary | Statement: [Flight into Egypt, featuresCharacter, Mary]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mary Context triple: [Flight into Egypt, featuresCharacter, Mary]
-
A.
Mary
chosen
Mary is a central figure in Christianity, venerated as the mother of Jesus and often honored as the Virgin Mary.
-
B.
Mary
Mary is the given first name of Margaret Truman, the daughter of U.S. President Harry S. Truman and a noted author and singer.
-
C.
Mary
Mary is a minor character in Mark Twain's novel "The Adventures of Tom Sawyer," known as Tom's kind and well-behaved cousin.
-
D.
Mary
Mary is a feminine given name of Hebrew origin, widely used in English-speaking and many other cultures and historically associated with numerous religious and historical figures.
-
E.
Mary
Mary is the first name of the American writer better known as Flannery O'Connor, a major figure in 20th-century Southern Gothic literature.
- 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_69a4939c32488190a7ccd41cf0abb22b |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ace8b8688190ac065f92c017adec |
completed | March 1, 2026, 9:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac1cd167188190aa05992376553550 |
completed | March 7, 2026, 12:40 p.m. |
Created at: March 1, 2026, 7:39 p.m.