Triple
T29707844
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Balbec |
E751684
|
entity |
| Predicate | modeledInRealityBy |
P39262
|
FINISHED |
| Object | Cabourg Grand Hôtel |
—
|
NE NERFINISHED |
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: Cabourg Grand Hôtel | Statement: [Balbec, modeledInRealityBy, Cabourg Grand Hôtel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: modeledInRealityBy Context triple: [Balbec, modeledInRealityBy, Cabourg Grand Hôtel]
-
A.
modeledBy
Indicates that one entity serves as a model or representation of another, typically capturing its structure, behavior, or properties.
-
B.
hasRealModel
chosen
Indicates that an abstract, theoretical, or simplified entity is associated with a corresponding concrete or physically instantiated model in the real world.
-
C.
modeledWith
Indicates that something is represented, simulated, or described using a particular model, method, or modeling technique.
-
D.
conceptualizedBy
Indicates that something exists as an idea, model, or concept that was formed or defined by a particular agent.
-
E.
isModelledAfter
Indicates that one entity is created, designed, or structured based on the form, features, or principles of another entity.
- 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_69f0d62748848190b030d0a703629a7d |
completed | April 28, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f672d63fe08190a2bc6c7e69ffe66c |
completed | May 2, 2026, 9:55 p.m. |
| PD | Predicate disambiguation | batch_69f66ac1a4fc81909740d2e52fbe6970 |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 28, 2026, 7:28 p.m.