Triple
T15556851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Boleyn |
E370891
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Mary |
E893588
|
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: [Mary Boleyn, givenName, Mary]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mary Context triple: [Mary Boleyn, givenName, Mary]
-
A.
Mary
Mary is a significant urban and economic center in southeastern Turkmenistan, known for its role in the country’s natural gas and cotton industries.
-
B.
Mary
Mary is the central, titular figure evoked in Jimi Hendrix’s song “The Wind Cries Mary,” often interpreted as a symbol of lost love and melancholy.
-
C.
Mary
Mary is a central character in Ralph Vaughan Williams's opera "Hugh the Drover," serving as the romantic interest whose choices drive much of the plot.
-
D.
Mary
chosen
Mary is the given name of Mary Elizabeth Baird Bryan, an American figure known primarily in relation to her husband, politician William Jennings Bryan.
-
E.
Mary
Mary is the first name of American film actress Peggy Moran, who appeared in numerous Hollywood movies during the late 1930s and early 1940s.
- 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_69d85cc6cf40819091f4a5facee1ebe6 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04a97dbfc8190a98cbbac5e71ba88 |
completed | April 16, 2026, 2:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff4c39ffbc819089cea285e8145fa4 |
completed | May 9, 2026, 3:01 p.m. |
Created at: April 10, 2026, 4:09 a.m.