Triple
T36454727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malwiya Tower |
E898120
|
entity |
| Predicate | numberOfRamps |
P119068
|
FINISHED |
| Object | one continuous ramp |
—
|
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: one continuous ramp | Statement: [Malwiya Tower, numberOfRamps, one continuous ramp]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfRamps Context triple: [Malwiya Tower, numberOfRamps, one continuous ramp]
-
A.
containsRamps
chosen
Indicates that one entity includes or is equipped with one or more ramps as part of its structure or features.
-
B.
hasSpiralRampLength
Indicates the length measurement of a spiral-shaped ramp in the relationship.
-
C.
hasRampSpace
Indicates that a location or structure includes designated space for a ramp, allowing sloped access between different levels.
-
D.
hasVehicleLoadingRamps
Indicates that something is equipped with ramps specifically intended for loading or unloading vehicles.
-
E.
numberOfStairs
Indicates the quantity of stairs associated with or present in a given context or structure.
- 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_69f76e57f08481908593bd0bc34581c8 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0bf4b88190bdcfae9a14b51f0a |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:10 p.m.