Triple
T6081682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Palais de la Méditerranée |
E135536
|
entity |
| Predicate | isLocatedOnFrenchRiviera |
P68537
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Palais de la Méditerranée, isLocatedOnFrenchRiviera, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isLocatedOnFrenchRiviera Context triple: [Palais de la Méditerranée, isLocatedOnFrenchRiviera, true]
-
A.
locatedInMetropolitanFrance
Indicates that the subject is geographically situated within the territory of metropolitan (continental) France.
-
B.
isLocatedOn
Indicates that one entity exists at or is situated upon the surface or area of another entity.
-
C.
hasCityOnRightBank
Indicates that a city is located on the right bank of a specified river or watercourse.
-
D.
hasFrenchSector
Indicates that an entity includes, controls, or is associated with a sector or area designated as French.
-
E.
hasCityOnRiver
Indicates that a city is located on or along the course of a particular river.
- 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_69c0087ad31c8190ab936e0ff28614b6 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05774bc948190a446b27e83f7079b |
completed | March 22, 2026, 8:56 p.m. |
| PD | Predicate disambiguation | batch_69c049f21fe08190995df3c5c05fb8ea |
completed | March 22, 2026, 7:58 p.m. |
| PDg | Predicate description generation | batch_69c04e8d4a148190bd8f95caae978e1b |
completed | March 22, 2026, 8:18 p.m. |
Created at: March 22, 2026, 4:11 p.m.