Triple
T5139140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Booth |
E115902
|
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: [William Booth, familyName, Booth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Booth Context triple: [William 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.
Butler
Butler is a city in Pennsylvania that serves as the administrative and economic center of Butler County.
-
E.
Butler
Butler is a common English and Irish surname historically associated with nobility and service roles, borne by numerous notable figures in politics, law, and the arts.
- 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_69bd44459a988190a772a5c2ec6a1965 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd787acd18819087f09db885893c3e |
completed | March 20, 2026, 4:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bec4d342c8819088f67c01d3769a6d |
completed | March 21, 2026, 4:18 p.m. |
Created at: March 20, 2026, 1:43 p.m.