Triple
T26391296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London Road area of Reading |
E663418
|
entity |
| Predicate | roadNumberIncludes |
P160491
|
FINISHED |
| Object | A4 |
—
|
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: A4 | Statement: [London Road area of Reading, roadNumberIncludes, A4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roadNumberIncludes Context triple: [London Road area of Reading, roadNumberIncludes, A4]
-
A.
roadNumberType
Indicates the classification or type category assigned to a road’s identifying number (e.g., highway, route, local road).
-
B.
roadNumberWithinNH
Indicates that a road’s designated number is assigned within and specific to the New Hampshire (NH) road system.
-
C.
roadNumberRange
Indicates that a road is identified by a continuous range of road numbers between a specified minimum and maximum.
-
D.
hasRoadNumberStatus
Indicates that a road or route is associated with a specific status regarding its assigned road number (e.g., active, reserved, retired, or provisional).
-
E.
hasConnectingRoadNumber
Indicates that there exists a road connection between two locations or road segments identified by a specific road number.
- F. None of above. chosen
Provenance (4 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_69ee883823988190b418b111be28a44a |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69f610bf1e248190a186aed862513478 |
completed | May 2, 2026, 2:57 p.m. |
| PD | Predicate disambiguation | batch_69f5f800fa9c8190aab0962669fde8ac |
completed | May 2, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69f6018ceb1c8190a6a5f84071659a96 |
completed | May 2, 2026, 1:52 p.m. |
Created at: April 26, 2026, 11:26 p.m.