Triple
T268406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ristorante Caterina de’ Medici |
E5782
|
entity |
| Predicate | servesAlcohol |
P9634
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Ristorante Caterina de’ Medici, servesAlcohol, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesAlcohol Context triple: [Ristorante Caterina de’ Medici, servesAlcohol, yes]
-
A.
alsoServes
Indicates that an entity, in addition to its primary role or function, provides service or support to another specified entity or group.
-
B.
traditionalDrink
Indicates that one entity is a beverage customarily consumed within the culture, heritage, or longstanding practices associated with another entity.
-
C.
alcoholLevel
Indicates the measured concentration or amount of alcohol present in an entity (such as a person, substance, or environment).
-
D.
servesVenue
Indicates that an entity provides services or functions in support of a particular venue.
-
E.
servesAgeRange
Indicates that a service, product, or offering is intended for or applicable to entities within a specified age range.
- 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_69a2587daeb081909591b9d30f80a271 |
completed | Feb. 28, 2026, 2:52 a.m. |
| NER | Named-entity recognition | batch_69a25dae4a0c8190a66cf6ed3889851c |
completed | Feb. 28, 2026, 3:14 a.m. |
| PD | Predicate disambiguation | batch_69a25b70d99c819085d8381a313a2a34 |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a25d6ec9688190b2ba027756b57d25 |
completed | Feb. 28, 2026, 3:13 a.m. |
Created at: Feb. 28, 2026, 2:56 a.m.