Triple
T353396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spruce Street (Philadelphia) |
E7490
|
entity |
| Predicate | hasLandUseAlong |
P2022
|
FINISHED |
| Object | residential |
—
|
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: residential | Statement: [Spruce Street (Philadelphia), hasLandUseAlong, residential]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLandUseAlong Context triple: [Spruce Street (Philadelphia), hasLandUseAlong, residential]
-
A.
locatedAlong
Indicates that one entity is situated adjacent to, or running beside, the length or course of another linear feature (such as a road, river, or railway).
-
B.
usesOnLand
Indicates that one entity employs or operates another entity specifically in a land-based context.
-
C.
hasLandCoverage
chosen
Indicates that a specified area or region is covered or occupied by a particular type of land surface or land use.
-
D.
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.
-
E.
hasLandform
Indicates that one entity possesses, contains, or is characterized by a particular natural landform.
- 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_69a2e7e696948190bebc966535995e45 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2eb80f524819093f4c2c18c3d615f |
completed | Feb. 28, 2026, 1:20 p.m. |
| PD | Predicate disambiguation | batch_69a2e9571bd88190b6fcb16f21604720 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.