Triple
T324856
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | September 11 attacks |
E6490
|
entity |
| Predicate | injuriesApproximate |
P661
|
FINISHED |
| Object | over 6000 |
—
|
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: over 6000 | Statement: [September 11 attacks, injuriesApproximate, over 6000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: injuriesApproximate Context triple: [September 11 attacks, injuriesApproximate, over 6000]
-
A.
hasInjuries
Indicates that an entity has sustained one or more physical or bodily injuries.
-
B.
approximates
Indicates that one entity is close to, but not exactly equal to, the value, form, or behavior of another entity.
-
C.
approximateMass
Indicates that one entity has a mass value that is an estimate or close approximation of the mass of another entity.
-
D.
damagedIn
Indicates that an entity has suffered harm, impairment, or destruction as a result of a specified event, process, or condition.
-
E.
casualtiesEstimate
chosen
Indicates an estimated number of people killed, injured, or otherwise harmed as a result of an event or incident.
- 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_69a2e7933d6c8190bb2592ad13286ef2 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2eb1a37c08190b1380f6bf8513a37 |
completed | Feb. 28, 2026, 1:18 p.m. |
| PD | Predicate disambiguation | batch_69a2e949364c8190bc2351f5413f5057 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.