Triple
T492973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Art Museum Steps |
E10227
|
entity |
| Predicate | hasTopLanding |
P8974
|
FINISHED |
| Object | platform overlooking Benjamin Franklin Parkway |
—
|
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: platform overlooking Benjamin Franklin Parkway | Statement: [Art Museum Steps, hasTopLanding, platform overlooking Benjamin Franklin Parkway]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTopLanding Context triple: [Art Museum Steps, hasTopLanding, platform overlooking Benjamin Franklin Parkway]
-
A.
hasLandings
Indicates that an entity has one or more associated landing events or landing locations.
-
B.
isToppedWith
Indicates that one entity serves as a topping placed on the surface of another entity.
-
C.
landingArea
chosen
Indicates that a location or surface serves as a designated area where something (such as an aircraft, object, or person) can land.
-
D.
hasFloor
Indicates that one entity possesses, includes, or is associated with a particular floor or level within a structure.
-
E.
hasHikingTrailToTop
Indicates that there exists a hiking trail leading from a starting location to the top or summit of a specified destination.
- 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_69a2e847df8481909239ec08ccf1e376 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f0faab4881909f65f172198b5bd2 |
completed | Feb. 28, 2026, 1:43 p.m. |
| PD | Predicate disambiguation | batch_69a2edf7ce008190836fb6ab5ea39375 |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.