Triple
T26635848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Palacio del Conde de Santovenia |
E668632
|
entity |
| Predicate | hasHotelName |
P120286
|
FINISHED |
| Object | Hotel Santa Isabel |
—
|
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: Hotel Santa Isabel | Statement: [Palacio del Conde de Santovenia, hasHotelName, Hotel Santa Isabel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHotelName Context triple: [Palacio del Conde de Santovenia, hasHotelName, Hotel Santa Isabel]
-
A.
hotelName
chosen
Indicates the specific name assigned to a hotel in the relationship.
-
B.
hotelBrand
Indicates that a hotel is affiliated with, operated by, or marketed under a specific hotel brand.
-
C.
reservationName
Indicates the name or title assigned to a specific reservation in the relationship.
-
D.
hadStationHotel
Indicates that a railway station possessed or was associated with a hotel facility serving its passengers or operations.
-
E.
hasHotelType
Indicates that a hotel is classified as belonging to a specific type or category (e.g., resort, boutique, hostel).
- 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_69ee9d0024b8819090a7c8cf669a3b6c |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69f6b903538481909cffcb6cc1cc0e70 |
completed | May 3, 2026, 2:54 a.m. |
| PD | Predicate disambiguation | batch_69f6b626120c819097c9ad04487570d7 |
completed | May 3, 2026, 2:42 a.m. |
Created at: April 27, 2026, 2:27 a.m.