Triple
T5373213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Church of the Resurrection |
E108899
|
entity |
| Predicate | largelyDestroyedIn |
P31453
|
FINISHED |
| Object | 1009 |
—
|
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: 1009 | Statement: [Church of the Resurrection, largelyDestroyedIn, 1009]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: largelyDestroyedIn Context triple: [Church of the Resurrection, largelyDestroyedIn, 1009]
-
A.
sufferedDestructionIn
chosen
Indicates that an entity experienced damage, ruin, or devastation during or as part of a specified event or period.
-
B.
buildingsDestroyed
Indicates that one or more buildings have been damaged to the point of destruction as a result of some event or action.
-
C.
sufferedDestructionOf
Indicates that one entity experienced damage, ruin, or loss as a result of the destruction of another entity.
-
D.
destroyedCity
Indicates that an entity has caused the complete or near-complete destruction of a city.
-
E.
mainCityDestroyed
Indicates that the primary or central city associated with an entity has been destroyed.
- 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_69bd440c77948190aad2a5f39b7b80f5 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd88801b188190b9ac35ed89167fa3 |
completed | March 20, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69bd846172788190969f24bc7503c05e |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2:03 p.m.