Triple
T337504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pantheon |
E6760
|
entity |
| Predicate | hasOculusDiameter |
P7302
|
FINISHED |
| Object | about 8.9 metres |
—
|
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: about 8.9 metres | Statement: [Pantheon, hasOculusDiameter, about 8.9 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOculusDiameter Context triple: [Pantheon, hasOculusDiameter, about 8.9 metres]
-
A.
approximateDiameter
chosen
Indicates that one entity specifies the estimated or rough measurement of another entity’s diameter.
-
B.
hasFieldOfView
Indicates that one entity possesses a visual coverage area within which it can perceive or detect other entities or regions.
-
C.
hasFormFactor
Indicates that one entity possesses or is characterized by a particular physical or structural form factor defined by another entity.
-
D.
hasMeanRadius
Indicates that an entity possesses a specified average radius measurement, typically representing the mean distance from its center to its surface.
-
E.
usesOpticsType
Indicates that one entity employs or is characterized by a specific type of optical system or technology.
- 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_69a2e79434908190a9d5afe415153ad9 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2eae23b0c819081f8bf9ac26685ab |
completed | Feb. 28, 2026, 1:17 p.m. |
| PD | Predicate disambiguation | batch_69a2e94f049881908f10bb6548a8bb2e |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.