Triple
T7895156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna Kournikova virus |
E183325
|
entity |
| Predicate | subjectLineUsed |
P78553
|
FINISHED |
| Object | Here you have, ;o) |
—
|
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: Here you have, ;o) | Statement: [Anna Kournikova virus, subjectLineUsed, Here you have, ;o)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectLineUsed Context triple: [Anna Kournikova virus, subjectLineUsed, Here you have, ;o)]
-
A.
subLine
Indicates that one line is a subordinate or component segment of another, typically representing a part–whole or hierarchical relationship between lines.
-
B.
lineUse
Indicates how a particular line (such as a route, track, or service line) is utilized or purposed within a system or network.
-
C.
usedTitleIn
Indicates that one entity employed or referenced another entity as a title in some context.
-
D.
lineUses
Indicates that a particular line (such as a route, service, or connection) makes use of or is implemented using a specified resource, infrastructure, or element.
-
E.
sentAs
chosen
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.
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_69ca828c474c8190a254d6499871eaff |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a174574819084270dbb6fcbb7fe |
completed | March 31, 2026, 3:05 a.m. |
| PD | Predicate disambiguation | batch_69cae92d94448190b4425bbfb64c658c |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:01 p.m.