Triple
T3302500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | State Street (Madison, Wisconsin) |
E69366
|
entity |
| Predicate | hasPedestrianOrientation |
P12663
|
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: [State Street (Madison, Wisconsin), hasPedestrianOrientation, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPedestrianOrientation Context triple: [State Street (Madison, Wisconsin), hasPedestrianOrientation, high]
-
A.
hasPedestrianPriority
Indicates that pedestrians are given precedence or right-of-way over other road users in a particular context or area.
-
B.
hasPedestrianPhase
Indicates that a traffic signal includes a dedicated phase during which pedestrians are allowed to cross.
-
C.
hasPedestrianCharacter
Indicates that something possesses qualities, features, or behavior characteristic of pedestrians or pedestrian use.
-
D.
hasOrientation
chosen
Indicates that one entity is positioned or directed in a specific spatial or conceptual alignment relative to a reference frame or another entity.
-
E.
hasPedestrianTrafficLevel
Indicates the level or intensity of pedestrian traffic associated with a given location or pathway.
- 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_69ad859e529c8190a404273f53cb487d |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb0a9450481909f0d630e5593085e |
completed | March 8, 2026, 5:23 p.m. |
| PD | Predicate disambiguation | batch_69ada42625308190be257f16a623a410 |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:11 p.m.