Triple
T10275394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nunnery Quadrangle |
E240950
|
entity |
| Predicate | misleadingName |
P41085
|
FINISHED |
| Object | not actually a nunnery |
—
|
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: not actually a nunnery | Statement: [Nunnery Quadrangle, misleadingName, not actually a nunnery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: misleadingName Context triple: [Nunnery Quadrangle, misleadingName, not actually a nunnery]
-
A.
misrepresentedAs
chosen
Indicates that one entity is falsely or inaccurately presented, portrayed, or described as another entity or in another way.
-
B.
notOfficialNameOf
Indicates that a given name or label is used for an entity but is not its official or formally recognized name.
-
C.
misidentifiedAs
Indicates that one entity has been incorrectly recognized, labeled, or understood as another, distinct entity.
-
D.
usesNameDueTo
Indicates that one entity adopts or applies a particular name for another entity specifically because of some motivating reason, circumstance, or dependency.
-
E.
hasTradeName
Indicates that a product, substance, or entity is known or marketed under a specific commercial or brand name.
- 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_69d381a94c1881908fc38fc263d9b9c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d28b6cd4819084a7a5c1893b5ad8 |
completed | April 7, 2026, 9:46 a.m. |
| PD | Predicate disambiguation | batch_69d4d1ef6e6c81908a8ee52e4d28127b |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:37 a.m.