Triple
T9007914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sendai Castle ruins |
E215390
|
entity |
| Predicate | damageHistory |
P81550
|
FINISHED |
| Object | suffered damage during Meiji period military use |
—
|
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: suffered damage during Meiji period military use | Statement: [Sendai Castle ruins, damageHistory, suffered damage during Meiji period military use]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: damageHistory Context triple: [Sendai Castle ruins, damageHistory, suffered damage during Meiji period military use]
-
A.
damageAssociatedWith
Indicates a relationship where one entity is linked to causing, contributing to, or being responsible for damage affecting another entity.
-
B.
damageLeadsTo
Indicates that one instance of damage causally results in or contributes to another specified outcome or condition.
-
C.
damageYear
Indicates the year in which the damage to an entity occurred or was recorded.
-
D.
sufferedDamageTo
Indicates that one entity has experienced harm, loss, or deterioration affecting another entity or one of its parts.
-
E.
damageDescription
chosen
Indicates a textual description of the nature, extent, or characteristics of damage associated with an entity 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_69ca83a2bf088190986ee7a8eb90407d |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc69bdc5fc819081015f4adacf9fd4 |
completed | April 1, 2026, 12:41 a.m. |
| PD | Predicate disambiguation | batch_69cc5edf84408190aa5f57cb8bfd00e1 |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:05 p.m.