Triple
T27756199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LoveLetter |
E701337
|
entity |
| Predicate | socialEngineeringTheme |
P79631
|
FINISHED |
| Object | romantic love |
—
|
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: romantic love | Statement: [LoveLetter, socialEngineeringTheme, romantic love]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: socialEngineeringTheme Context triple: [LoveLetter, socialEngineeringTheme, romantic love]
-
A.
usesSocialEngineeringTheme
chosen
Indicates that an action or communication employs social engineering tactics or themes to influence, deceive, or manipulate a target.
-
B.
methodOfInfiltration
Indicates the specific technique or approach used to secretly gain access to or penetrate a target, system, or organization.
-
C.
exploits
Indicates that one entity unfairly or selfishly uses another entity or resource for its own advantage or benefit.
-
D.
attackToolExample
Indicates that a specific tool or method is used as an example of how an attack is or can be carried out.
-
E.
earlyExploitationBy
Indicates that one entity takes unfair advantage of another at an early stage of their interaction, development, or process.
- 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_69ef6a5193808190816eb7d0020b2d87 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f67f0488bc819089fbd2d2478158d3 |
completed | May 2, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69f67e3ed894819094c067c1ef624951 |
completed | May 2, 2026, 10:44 p.m. |
Created at: April 27, 2026, 4:23 p.m.