Triple
T5935216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Processional Way |
E132025
|
entity |
| Predicate | pavementThickness |
P9690
|
FINISHED |
| Object | about 0.9 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: about 0.9 meters | Statement: [Processional Way, pavementThickness, about 0.9 meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pavementThickness Context triple: [Processional Way, pavementThickness, about 0.9 meters]
-
A.
pavedWith
Indicates that a surface or area is covered or constructed using a specified material as its paving.
-
B.
streetMaterial
Indicates the material composition from which a street or road surface is made.
-
C.
isPavedMostOfWay
Indicates that a route or path is surfaced with pavement for the majority of its length, though not necessarily entirely.
-
D.
hasPavementPattern
Indicates that an entity possesses or is characterized by a specific pattern or design in its pavement surface.
-
E.
thickness
chosen
Indicates the measure of how deep or wide an object or layer is from one surface or side to its opposite.
- 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_69c0085c55dc8190aa90e242c956e2fa |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03f26f51881908cc253fe5775a1fc |
completed | March 22, 2026, 7:12 p.m. |
| PD | Predicate disambiguation | batch_69c03355caf08190b960563a1aed23f9 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 4:01 p.m.