Triple
T4859649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Griffin Street (Dallas) |
E108624
|
entity |
| Predicate | hasAreaCharacter |
P6822
|
FINISHED |
| Object | urban core |
—
|
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: urban core | Statement: [Griffin Street (Dallas), hasAreaCharacter, urban core]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAreaCharacter Context triple: [Griffin Street (Dallas), hasAreaCharacter, urban core]
-
A.
hasAreaType
chosen
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
B.
hasAreaRange
Indicates that something’s area falls within a specified minimum-to-maximum range.
-
C.
hasLandmarkArea
Indicates that a specified area is designated as the landmark area associated with a particular entity or location.
-
D.
hasCivilArea
Indicates that an administrative or political entity encompasses or is associated with a specific civil (local administrative) area.
-
E.
hasAreaTotal
Indicates the total surface area associated with an entity, typically measured over its entire extent.
- 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_69bd440b965081908b0557721cae6338 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6d5c92148190a314707bdd3ff30f |
completed | March 20, 2026, 3:53 p.m. |
| PD | Predicate disambiguation | batch_69bd6c27334481909ba8ac80854f7d8e |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:26 p.m.