Triple
T27756235
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Melissa virus |
E701338
|
entity |
| Predicate | emailsSentPerInfection |
P163550
|
FINISHED |
| Object | up to 50 recipients |
—
|
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: up to 50 recipients | Statement: [Melissa virus, emailsSentPerInfection, up to 50 recipients]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: emailsSentPerInfection Context triple: [Melissa virus, emailsSentPerInfection, up to 50 recipients]
-
A.
numberOfRecipientsPerMonth
Indicates the quantity of recipients associated with an entity for each month.
-
B.
sentMembersTo
Indicates that one entity dispatched or assigned its members to another entity or location.
-
C.
infectsOrder
Indicates that one entity causes an infection in, or transmits a disease to, another entity in a specified order or sequence.
-
D.
typicalNumberOfRecipientsPerYear
Indicates the usual or average count of recipients involved in or affected by something within a one-year period.
-
E.
sentAs
Indicates that one entity was transmitted, dispatched, or delivered in the form, role, or capacity of another (e.g., an item or message being sent as something specific).
- F. None of above. chosen
Provenance (4 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_69ef6a5193808190816eb7d0020b2d87 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f6397b64f881909d811225e57aac5e |
completed | May 2, 2026, 5:50 p.m. |
| PD | Predicate disambiguation | batch_69f6370c8c7c8190a02ea82847bb6e76 |
completed | May 2, 2026, 5:40 p.m. |
| PDg | Predicate description generation | batch_69f63893cc188190883ac9321a95d2dc |
completed | May 2, 2026, 5:46 p.m. |
Created at: April 27, 2026, 4:23 p.m.