Triple
T31632503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Calle del Laurel |
E807205
|
entity |
| Predicate | hasDensityOfBars |
P81830
|
FINISHED |
| Object | high |
—
|
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: high | Statement: [Calle del Laurel, hasDensityOfBars, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDensityOfBars Context triple: [Calle del Laurel, hasDensityOfBars, high]
-
A.
hasNumberOfRestaurantsAndBars
Indicates the total count of restaurants and bars associated with a given entity.
-
B.
hasHighDensityOf
chosen
Indicates that one entity contains or exhibits a large concentration or amount of another entity within a given area, volume, or context.
-
C.
hasBars
Indicates that one entity possesses, contains, or is equipped with bars as a defining feature or component.
-
D.
hasNumberOfBars
Indicates the specific count of bars associated with or contained in an entity.
-
E.
hasBarStrength
Indicates that one entity possesses or exhibits a certain level or measure of bar-related strength or robustness in relation to another entity or context.
- 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_69f348d892948190915f8facacb9568c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fe6739d4dc8190ae7505c089bbac29 |
completed | May 8, 2026, 10:44 p.m. |
| PD | Predicate disambiguation | batch_69fe6541dffc81909c66a61ba69f38fc |
completed | May 8, 2026, 10:35 p.m. |
Created at: April 30, 2026, 10:45 p.m.