Triple
T30813757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eyad Ismoil |
E784714
|
entity |
| Predicate | victimOfCrimeLocation |
P59762
|
FINISHED |
| Object | World Trade Center, New York City |
—
|
NE NERFINISHED |
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: World Trade Center, New York City | Statement: [Eyad Ismoil, victimOfCrimeLocation, World Trade Center, New York City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: victimOfCrimeLocation Context triple: [Eyad Ismoil, victimOfCrimeLocation, World Trade Center, New York City]
-
A.
crimeLocation
chosen
Indicates that a crime occurred at, or is associated with, a particular location.
-
B.
targetOfCrime
Indicates that the subject is the person, organization, or entity against whom the referenced crime is committed.
-
C.
positionOnCrime
Indicates a stance, opinion, or policy position that an entity holds regarding crime or crime-related issues.
-
D.
victimsOfCrime
Indicates that the subject has suffered harm or loss as a result of a criminal act committed by the object.
-
E.
placeOfTheft
Indicates the location where a theft occurred.
- 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_69f224b4eda48190bd212ce4f3901e56 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6a0ea04888190ac3a813b603bcb5c |
completed | May 3, 2026, 1:12 a.m. |
| PD | Predicate disambiguation | batch_69f69fe463248190aa78128abeab1183 |
completed | May 3, 2026, 1:07 a.m. |
Created at: April 29, 2026, 8:43 p.m.