Triple
T24572730
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aurelian Walls segment near Porta Maggiore |
E608003
|
entity |
| Predicate | thicknessApprox |
P16570
|
FINISHED |
| Object | 3 meters to 4 meters |
—
|
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: 3 meters to 4 meters | Statement: [Aurelian Walls segment near Porta Maggiore, thicknessApprox, 3 meters to 4 meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: thicknessApprox Context triple: [Aurelian Walls segment near Porta Maggiore, thicknessApprox, 3 meters to 4 meters]
-
A.
thickness
Indicates the measure of how deep or wide an object or layer is from one surface or side to its opposite.
-
B.
bodyThickness
Indicates the measured or relative thickness of an entity’s body in the context of a comparison or description.
-
C.
wallThicknessComparedTo
Indicates how the thickness of one wall relates to the thickness of another wall, typically in terms of being greater, equal, or less.
-
D.
depthMetresApprox
chosen
Indicates an approximate measurement of how deep something is in metres, rather than an exact value.
-
E.
skinThickness
Indicates the measured thickness of an entity’s skin, typically quantifying how thick its outer tissue layer is.
- 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_69e2c4cdab6c8190aae6e5d3de55c95e |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2a9269360819090c44f4483c3533c |
completed | April 30, 2026, 12:58 a.m. |
| PD | Predicate disambiguation | batch_69f2a6c1f07081908edf0b521767e79b |
completed | April 30, 2026, 12:48 a.m. |
Created at: April 18, 2026, 2:28 a.m.