Triple
T13742200
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Strangeways Prison, Manchester |
E330111
|
entity |
| Predicate | damageFromRiot |
P78831
|
FINISHED |
| Object | extensive structural damage |
—
|
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: extensive structural damage | Statement: [Strangeways Prison, Manchester, damageFromRiot, extensive structural damage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: damageFromRiot Context triple: [Strangeways Prison, Manchester, damageFromRiot, extensive structural damage]
-
A.
damageTo
Indicates a relationship where one entity causes harm, loss, or deterioration to another entity.
-
B.
damageLeadsTo
Indicates that one instance of damage causally results in or contributes to another specified outcome or condition.
-
C.
damageAssociatedWith
chosen
Indicates a relationship where one entity is linked to causing, contributing to, or being responsible for damage affecting another entity.
-
D.
damageEffect
Indicates that one entity causes harm, reduction, or deterioration to another entity or its properties.
-
E.
damageAdjusted
Indicates that the amount of damage has been modified from its original value, typically to account for mitigating or amplifying factors.
- 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_69d80772315881908f980cae40d91664 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69de020855ec8190a60fa1cb761f2e68 |
completed | April 14, 2026, 8:59 a.m. |
| PD | Predicate disambiguation | batch_69dbbe950b148190ba0df8a749269ec6 |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 9:55 p.m.