Triple
T17001983
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dzhokhar Tsarnaev |
E412468
|
entity |
| Predicate | numberOfPeopleInjuredInCrime |
P63693
|
FINISHED |
| Object | hundreds |
—
|
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: hundreds | Statement: [Dzhokhar Tsarnaev, numberOfPeopleInjuredInCrime, hundreds]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPeopleInjuredInCrime Context triple: [Dzhokhar Tsarnaev, numberOfPeopleInjuredInCrime, hundreds]
-
A.
numberOfVictimsInjured
chosen
Indicates the count of victims who sustained injuries as a result of the event or incident.
-
B.
numberOfPerpetrators
Indicates the count of distinct individuals who carried out or participated in a particular act, event, or offense.
-
C.
numberOfGunshotWounds
Indicates the count of gunshot wounds associated with a particular entity or event.
-
D.
numberOfPeopleAccused
Indicates the count of individuals who are formally alleged to have committed a particular act or offense.
-
E.
numberOfPeopleShot
Indicates the count of individuals who were shot in a particular event or context.
- 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_69d886cb581c8190ab05f4b429c9cd85 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d37ec2b48190b4f997899d887ba8 |
completed | April 18, 2026, 6:54 p.m. |
| PD | Predicate disambiguation | batch_69e35d552bc08190af17ef7659e094ef |
completed | April 18, 2026, 10:30 a.m. |
Created at: April 10, 2026, 5:32 a.m.