Triple
T30657012
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nidal Ayyad |
E780411
|
entity |
| Predicate | notableCrimeTarget |
P207293
|
FINISHED |
| Object | World Trade Center |
E500683
|
NE 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: World Trade Center | Statement: [Nidal Ayyad, notableCrimeTarget, World Trade Center]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableCrimeTarget Context triple: [Nidal Ayyad, notableCrimeTarget, World Trade Center]
-
A.
targetOfCrime
Indicates that the subject is the person, organization, or entity against whom the referenced crime is committed.
-
B.
notableHeistTarget
Indicates that an entity is a significant or high-profile target of a heist or major theft.
-
C.
lawEnforcementTarget
Indicates that an entity is the focus or object of attention, investigation, or action by law enforcement authorities.
-
D.
crimeLocation
Indicates that a crime occurred at, or is associated with, a particular location.
-
E.
notableTheft
Indicates that an entity is involved in a theft event that is widely recognized or significant in some notable way.
- F. None of above. chosen
Provenance (5 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_69f224a6d10481909290be1a00fc83b3 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2870dc8e708190b95fc77f00591801 |
completed | June 9, 2026, 8 p.m. |
| PD | Predicate disambiguation | batch_6a0379e2fac0819089b522db3260028c |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7ee0388190a29faeb5cdb0950a |
completed | May 12, 2026, 7:16 p.m. |
Created at: April 29, 2026, 8:30 p.m.