Triple
T59139
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huntington, New York |
E1170
|
entity |
| Predicate | isSuburban |
P749
|
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: [Huntington, New York, isSuburban, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isSuburban Context triple: [Huntington, New York, isSuburban, yes]
-
A.
isRural
Indicates that something is located in, characteristic of, or associated with a countryside or non-urban area.
-
B.
hasUrbanFeature
Indicates that a place or area possesses a specific urban element or infrastructure feature (such as roads, parks, or buildings) as part of its built environment.
-
C.
urbanAreaType
chosen
Indicates the classification of an area based on its urban characteristics or development type (e.g., city, town, suburb, metropolitan region).
-
D.
hasTown
Indicates that one entity possesses, contains, or is associated with a town as part of its structure, jurisdiction, or composition.
-
E.
hasSubdivision
Indicates that one entity is divided into and contains another entity as one of its constituent parts or administrative units.
- 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_69a24a552ef88190a0df287d68c65cba |
completed | Feb. 28, 2026, 1:52 a.m. |
| NER | Named-entity recognition | batch_69a250e401288190ba12322c9c5f07c9 |
completed | Feb. 28, 2026, 2:20 a.m. |
| PD | Predicate disambiguation | batch_69a24e9f40908190a2f4a2111469b733 |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 1:55 a.m.