Triple
T7518116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hôtel l’Elysée Val d’Europe |
E177696
|
entity |
| Predicate | proximityToAttraction |
P53174
|
FINISHED |
| Object | close to Disneyland Paris |
—
|
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: close to Disneyland Paris | Statement: [Hôtel l’Elysée Val d’Europe, proximityToAttraction, close to Disneyland Paris]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: proximityToAttraction Context triple: [Hôtel l’Elysée Val d’Europe, proximityToAttraction, close to Disneyland Paris]
-
A.
proximityToLandmark
chosen
Indicates a spatial relationship where one entity is located near or close to a specified landmark.
-
B.
nearbyTo
Indicates that one entity is located close in distance or position to another entity.
-
C.
closestTo
Indicates that one entity is nearer in distance to a reference entity than any other comparable entity.
-
D.
closerTo
Indicates that one entity is at a smaller distance to a reference entity than another entity is.
-
E.
isCloseTo
Indicates that one entity is physically or conceptually near another, within a relatively short distance or range.
- 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_69c69f2891148190a484f3b8222c6f1b |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5f850c081909e697219071293fc |
completed | March 27, 2026, 9:26 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d44e9481909813e073b194f6f4 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:46 p.m.