Triple
T8504695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1921 Tulsa Race Massacre |
E201303
|
entity |
| Predicate | homesDestroyed |
P1583
|
FINISHED |
| Object | more than 1,000 |
—
|
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: more than 1,000 | Statement: [1921 Tulsa Race Massacre, homesDestroyed, more than 1,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: homesDestroyed Context triple: [1921 Tulsa Race Massacre, homesDestroyed, more than 1,000]
-
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.
areaDestroyed
Indicates that a specified portion or region has been damaged or ruined to the point of destruction.
-
C.
destroyedCity
Indicates that an entity has caused the complete or near-complete destruction of a city.
-
D.
numberOfDistrictsDestroyed
Indicates the quantity of districts that have been destroyed in a given context or event.
-
E.
numberOfDistrictsHeavilyDamaged
Indicates the count of districts that have sustained severe or heavy damage in a given context or event.
- 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_69ca831fe47c8190b5c57b456d2aefa0 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe5d8b7208190b199c56bf366c692 |
completed | March 31, 2026, 3:18 p.m. |
| PD | Predicate disambiguation | batch_69cbd10cfd208190a519049fad32c508 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:14 p.m.