Triple
T8681791
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1940 Hundred Regiments Offensive |
E206054
|
entity |
| Predicate | damageInflicted |
P32756
|
FINISHED |
| Object | destruction of railway tracks and bridges |
—
|
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: destruction of railway tracks and bridges | Statement: [1940 Hundred Regiments Offensive, damageInflicted, destruction of railway tracks and bridges]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: damageInflicted Context triple: [1940 Hundred Regiments Offensive, damageInflicted, destruction of railway tracks and bridges]
-
A.
damageTo
chosen
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.
damageDescription
Indicates a textual description of the nature, extent, or characteristics of damage associated with an entity or event.
-
D.
damageAssociatedWith
Indicates a relationship where one entity is linked to causing, contributing to, or being responsible for damage affecting another entity.
-
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_69ca835379688190aa06b9d98e684d58 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc4ae82e508190b0243328e98fcb1d |
completed | March 31, 2026, 10:30 p.m. |
| PD | Predicate disambiguation | batch_69cc4567b5c881908d9ec5dcfc783fac |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:32 p.m.