Triple
T26311030
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mon Paris Couture |
E661819
|
entity |
| Predicate | positioningVsMonParis |
P175960
|
FINISHED |
| Object | brighter interpretation |
—
|
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: brighter interpretation | Statement: [Mon Paris Couture, positioningVsMonParis, brighter interpretation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: positioningVsMonParis Context triple: [Mon Paris Couture, positioningVsMonParis, brighter interpretation]
-
A.
positionInParadiso
Indicates the specific location or rank an entity holds within the structure or hierarchy of Paradiso.
-
B.
directionFromParis
Indicates the cardinal or relative compass direction of an entity as measured from Paris toward that entity.
-
C.
primaryStopInParis
Indicates that an entity’s main or most significant stop, visit, or stopover occurs in Paris.
-
D.
positionOften
Indicates that one entity frequently holds, occupies, or is located at a particular position relative to another entity or context.
-
E.
integratedIntoParis
Indicates that one entity has been incorporated or merged into the administrative, social, or structural framework of Paris.
- 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_69ee812dacfc81908484aade9120fba9 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f6db3606808190a80c6e9f5da5b33e |
completed | May 3, 2026, 5:20 a.m. |
| PD | Predicate disambiguation | batch_69f6d82adfa481908a5e196d2e18c73f |
completed | May 3, 2026, 5:07 a.m. |
| PDg | Predicate description generation | batch_69f6db1bc348819097c844f76e2fa4fe |
completed | May 3, 2026, 5:20 a.m. |
Created at: April 26, 2026, 10:22 p.m.