Triple
T9010987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Ann Booth |
E215470
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Booth |
E21232
|
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: Booth | Statement: [Mary Ann Booth, familyName, Booth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Booth Context triple: [Mary Ann Booth, familyName, Booth]
-
A.
Booth
chosen
Booth is a common English surname historically associated with notable figures in theater, politics, and American history.
-
B.
Buckley
Buckley is a small city in Washington State known for its rural character and proximity to Mount Rainier.
-
C.
Buckley
Buckley is a surname most prominently associated with William F. Buckley Jr., the influential American conservative author and founder of National Review.
-
D.
Buckley
Buckley is a small town in Flintshire, northeast Wales, known historically for its brickworks and coal mining industries.
-
E.
Burris
Burris is a surname and given name, often considered a variant of "Burr," borne by various individuals across sports, politics, and entertainment.
- 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_69ca83a2bf088190986ee7a8eb90407d |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc69c1571881908d0b144786b5ee1f |
completed | April 1, 2026, 12:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfdb9dca848190952427bb5712081f |
completed | April 3, 2026, 3:24 p.m. |
Created at: March 30, 2026, 7:06 p.m.