Triple
T392426
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benjamin Franklin Parkway |
E8908
|
entity |
| Predicate | hasLayout |
P10827
|
FINISHED |
| Object | diagonal boulevard |
—
|
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: diagonal boulevard | Statement: [Benjamin Franklin Parkway, hasLayout, diagonal boulevard]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLayout Context triple: [Benjamin Franklin Parkway, hasLayout, diagonal boulevard]
-
A.
hasWidth
Indicates that an entity possesses a specific measurement or extent along its width dimension.
-
B.
hasInitial
Indicates that one entity possesses or is associated with the first letter or starting character of another entity’s name or value.
-
C.
hasStyle
Indicates that an entity possesses, exhibits, or is characterized by a particular style or manner.
-
D.
hasView
Indicates that one entity provides a visual perspective or outlook onto another entity or scene.
-
E.
hasHeight
Indicates that one entity possesses a specific vertical measurement or stature.
- 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_69a2e7f55c60819097aff65ea2ca2832 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec7492288190bf33c9c869a0710f |
completed | Feb. 28, 2026, 1:24 p.m. |
| PD | Predicate disambiguation | batch_69a2e96a8ca48190abbd8de9b02c115c |
completed | Feb. 28, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69a2ea2dc3088190a2aeb4496aff3582 |
completed | Feb. 28, 2026, 1:14 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.