Triple
T849951
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Fire of London |
E18361
|
entity |
| Predicate | numberOfChurchesDestroyed |
P12144
|
FINISHED |
| Object | around 87 parish churches |
—
|
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: around 87 parish churches | Statement: [Great Fire of London, numberOfChurchesDestroyed, around 87 parish churches]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfChurchesDestroyed Context triple: [Great Fire of London, numberOfChurchesDestroyed, around 87 parish churches]
-
A.
buildingsDestroyed
Indicates that one or more buildings have been damaged to the point of destruction as a result of some event or action.
-
B.
hasNumberOfChurches
chosen
Indicates the relationship that specifies how many churches are associated with a given entity.
-
C.
mainCityDestroyed
Indicates that the primary or central city associated with an entity has been destroyed.
-
D.
areaDestroyed
Indicates that a specified portion or region has been damaged or ruined to the point of destruction.
-
E.
earlierSynagogueDestroyedBy
Indicates that an earlier synagogue was destroyed as a result of the actions or influence of the specified agent or cause.
- 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_69a4938b04208190b82e1df6b572c548 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac215194819099e6bc1b5df58fb3 |
completed | March 1, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69a4aa807adc8190ad808a573cf8e923 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:38 p.m.