Triple
T21789591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1939 Erzincan earthquake |
E537933
|
entity |
| Predicate | buildingsDestroyedInErzincan |
P1583
|
FINISHED |
| Object | most of the city |
—
|
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: most of the city | Statement: [1939 Erzincan earthquake, buildingsDestroyedInErzincan, most of the city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: buildingsDestroyedInErzincan Context triple: [1939 Erzincan earthquake, buildingsDestroyedInErzincan, most of the city]
-
A.
buildingsDestroyed
chosen
Indicates that one or more buildings have been damaged to the point of destruction as a result of some event or action.
-
B.
affectedTurkishProvince
Indicates that a Turkish province is impacted or influenced by a specified event, condition, or entity.
-
C.
sufferedDestructionIn
Indicates that an entity experienced damage, ruin, or devastation during or as part of a specified event or period.
-
D.
landmarksDestroyed
Indicates that certain notable or significant physical landmarks have been damaged or completely destroyed.
-
E.
originalBuildingDestroyedBy
Indicates that the original building was destroyed as a result of the actions or effects 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_69e0c47198f881908cb0d237266c10e9 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f0621ed2b481909e3df17aee9cd8f7 |
completed | April 28, 2026, 7:30 a.m. |
| PD | Predicate disambiguation | batch_69e6be751ce881909badced245ef76c7 |
completed | April 21, 2026, 12:01 a.m. |
Created at: April 16, 2026, 6:52 p.m.