Triple
T32987612
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thomas Jefferson statue (removed 2020) |
E843990
|
entity |
| Predicate | wasVandalized |
P175461
|
FINISHED |
| Object | yes |
—
|
LITERAL 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: yes | Statement: [Thomas Jefferson statue (removed 2020), wasVandalized, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wasVandalized Context triple: [Thomas Jefferson statue (removed 2020), wasVandalized, yes]
-
A.
wasAttainted
Indicates that a person was legally declared guilty of a serious offense (often treason or felony), resulting in loss of civil rights, property, or titles.
-
B.
damagedBy
Indicates that one entity has caused harm, impairment, or deterioration to another entity.
-
C.
damagedIn
Indicates that an entity has suffered harm, impairment, or destruction as a result of a specified event, process, or condition.
-
D.
defacedWith
Indicates that one entity has been damaged, marred, or vandalized using another entity as the means or material of defacement.
-
E.
wasCensored
Indicates that an entity’s content, expression, or communication was suppressed, altered, or restricted by an authority or controlling party.
- F. None of above. chosen
Provenance (4 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_69f3494c6f9c8190a255409fce8b1d3b |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d1e3d480819085eb940623e8c3d6 |
completed | May 3, 2026, 4:41 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe5f93c8190995c53dbbe380a32 |
completed | May 3, 2026, 4:32 a.m. |
| PDg | Predicate description generation | batch_69f6d0d331dc8190be5aa6bfc6365e67 |
completed | May 3, 2026, 4:36 a.m. |
Created at: May 1, 2026, 1:22 a.m.