Triple
T20415727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucentio |
E500706
|
entity |
| Predicate | occupationInDisguise |
P140052
|
FINISHED |
| Object | tutor |
—
|
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: tutor | Statement: [Lucentio, occupationInDisguise, tutor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: occupationInDisguise Context triple: [Lucentio, occupationInDisguise, tutor]
-
A.
disguisedAs
Indicates that one entity is intentionally presenting itself as, or made to appear as, another entity in order to conceal its true identity.
-
B.
usesMasksOrDisguises
Indicates that an entity employs masks, costumes, or other forms of disguise to conceal or alter its identity in the context of an action or interaction.
-
C.
nationalityInDisguise
Indicates that an entity’s true nationality is concealed or misrepresented, often by adopting or appearing to belong to a different nationality.
-
D.
hostsCharacterInDisguise
Indicates that a host entity accommodates or presents a character who is concealed under a false identity or disguise.
-
E.
fictionalOccupation
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
- 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_69e0b4a935588190b9446a99b37ced44 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67a437eec8190a20c89a236dd5bc0 |
completed | April 20, 2026, 7:10 p.m. |
| PD | Predicate disambiguation | batch_69e5766df0008190a73c4f613c29678f |
completed | April 20, 2026, 12:42 a.m. |
| PDg | Predicate description generation | batch_69e58d7481508190a87c8b88f9df9879 |
completed | April 20, 2026, 2:20 a.m. |
Created at: April 16, 2026, 11:30 a.m.