Triple
T7800233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terry Nichols |
E180411
|
entity |
| Predicate | numberOfDeathsCaused |
P1785
|
FINISHED |
| Object | 168 (Oklahoma City bombing) |
—
|
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: 168 (Oklahoma City bombing) | Statement: [Terry Nichols, numberOfDeathsCaused, 168 (Oklahoma City bombing)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfDeathsCaused Context triple: [Terry Nichols, numberOfDeathsCaused, 168 (Oklahoma City bombing)]
-
A.
causeOfDeath
Indicates the specific factor, event, or condition that directly resulted in an entity’s death.
-
B.
deathToll
chosen
Indicates the number of deaths resulting from a particular event, situation, or cause.
-
C.
deathTollEstimate
Indicates an estimated number of deaths attributed to a particular event, cause, or period.
-
D.
reasonForDeath
Indicates the cause, circumstance, or condition that led to an entity’s death.
-
E.
fatalitiesCategory
Indicates the classification of deaths associated with an event, incident, or condition into a specific category or severity level.
- 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_69ca827e50cc8190a92a733577184938 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf78a6d88819093f83528fe88b182 |
completed | March 30, 2026, 10:22 p.m. |
| PD | Predicate disambiguation | batch_69cae9111b2481909684a2d4aa4831c2 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:33 p.m.