Triple
T849952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Fire of London |
E18361
|
entity |
| Predicate | numberOfCityGatesDestroyed |
P21097
|
FINISHED |
| Object | several city gates |
—
|
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: several city gates | Statement: [Great Fire of London, numberOfCityGatesDestroyed, several city gates]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCityGatesDestroyed Context triple: [Great Fire of London, numberOfCityGatesDestroyed, several city gates]
-
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.
mainCityDestroyed
Indicates that the primary or central city associated with an entity has been destroyed.
-
C.
areaDestroyed
Indicates that a specified portion or region has been damaged or ruined to the point of destruction.
-
D.
numberOfExplosions
Indicates the count of distinct explosion events associated with an entity or situation.
-
E.
estimatedTeaChestsDestroyed
Indicates the estimated number of tea chests that were destroyed in a given event or context.
- F. None of above. chosen
Provenance (4 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. |
| PDg | Predicate description generation | batch_69a4abb157d08190a7d7281eb3f1b788 |
completed | March 1, 2026, 9:12 p.m. |
Created at: March 1, 2026, 7:38 p.m.