Triple
T20256807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Equestrian statue of Robert E. Lee (Richmond) |
E498722
|
entity |
| Predicate | wasVandalizedDuring |
P992
|
FINISHED |
| Object | 2020 protests |
—
|
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: 2020 protests | Statement: [Equestrian statue of Robert E. Lee (Richmond), wasVandalizedDuring, 2020 protests]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wasVandalizedDuring Context triple: [Equestrian statue of Robert E. Lee (Richmond), wasVandalizedDuring, 2020 protests]
-
A.
destroyedDuring
Indicates that one entity was destroyed in the course of, or as a consequence of, a specified event or time period.
-
B.
damagedBy
Indicates that one entity has caused harm, impairment, or deterioration to another entity.
-
C.
damagedIn
chosen
Indicates that an entity has suffered harm, impairment, or destruction as a result of a specified event, process, or condition.
-
D.
sufferedDamageTo
Indicates that one entity has experienced harm, loss, or deterioration affecting another entity or one of its parts.
-
E.
weakenedDuring
Indicates that an entity’s strength, intensity, or effectiveness is reduced over the course of a specified time period or event.
- F. None of above.
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_69da6275fa6c8190952924930adee150 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e674c6693081908ff8bec12f212f7b |
completed | April 20, 2026, 6:47 p.m. |
| PD | Predicate disambiguation | batch_69e55b1b23f88190bdcbe2f81dd226dd |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 11, 2026, 11:41 p.m.