Triple
T438260
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Tower of the World Trade Center |
E10057
|
entity |
| Predicate | destructionCause |
P5325
|
FINISHED |
| Object | terrorist attack |
—
|
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: terrorist attack | Statement: [North Tower of the World Trade Center, destructionCause, terrorist attack]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: destructionCause Context triple: [North Tower of the World Trade Center, destructionCause, terrorist attack]
-
A.
hasCauseOfDestruction
chosen
Indicates that one entity is the cause or agent responsible for the destruction or damage of another entity.
-
B.
causeOf
Indicates that one entity brings about, produces, or is responsible for the occurrence or existence of another entity or event.
-
C.
reasonForDemolition
Indicates the cause, justification, or circumstance that led to a structure or object being demolished.
-
D.
destroyedDuring
Indicates that one entity was destroyed in the course of, or as a consequence of, a specified event or time period.
-
E.
damagedBy
Indicates that one entity has caused harm, impairment, or deterioration to another entity.
- 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_69a2e8465ef481909655c681b01e2986 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2ef283be881909444aaf257451747 |
completed | Feb. 28, 2026, 1:35 p.m. |
| PD | Predicate disambiguation | batch_69a2eddb98e081909efcf9f0a955a908 |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.