Triple
T23079133
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mahakaleshwar Temple, Ujjain |
E575417
|
entity |
| Predicate | damagedInHistoryBy |
P992
|
FINISHED |
| Object | repeated invasions and attacks |
—
|
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: repeated invasions and attacks | Statement: [Mahakaleshwar Temple, Ujjain, damagedInHistoryBy, repeated invasions and attacks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: damagedInHistoryBy Context triple: [Mahakaleshwar Temple, Ujjain, damagedInHistoryBy, repeated invasions and attacks]
-
A.
damagedIn
chosen
Indicates that an entity has suffered harm, impairment, or destruction as a result of a specified event, process, or condition.
-
B.
damagedBy
Indicates that one entity has caused harm, impairment, or deterioration to another entity.
-
C.
sufferedDestructionIn
Indicates that an entity experienced damage, ruin, or devastation during or as part of a specified event or period.
-
D.
sufferedDamageTo
Indicates that one entity has experienced harm, loss, or deterioration affecting another entity or one of its parts.
-
E.
sufferedDestructionOf
Indicates that one entity experienced damage, ruin, or loss as a result of the destruction of another entity.
- 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_69e245be28d48190ad1348d5a73db37d |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18c6572d88190afab0dfa960a85dd |
completed | April 29, 2026, 4:43 a.m. |
| PD | Predicate disambiguation | batch_69ef89e5ce748190b2c3ac3843484127 |
completed | April 27, 2026, 4:08 p.m. |
Created at: April 17, 2026, 3:56 p.m.