Triple
T11949350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spaghetti Junction |
E284387
|
entity |
| Predicate | hasNumberOfRoadsConnected |
P28807
|
FINISHED |
| Object | 18 |
—
|
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: 18 | Statement: [Spaghetti Junction, hasNumberOfRoadsConnected, 18]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfRoadsConnected Context triple: [Spaghetti Junction, hasNumberOfRoadsConnected, 18]
-
A.
hasConnectingRoadNumber
Indicates that there exists a road connection between two locations or road segments identified by a specific road number.
-
B.
hasNumberOfStreets
Indicates the relationship that specifies how many streets are associated with or contained within a given entity.
-
C.
hasRoads
Indicates that there exist constructed road connections linking the related entities.
-
D.
numberOfRoadways
chosen
Indicates the count of distinct roadways associated with or present at a given entity or location.
-
E.
numberOfConvergingStreets
Indicates the count of distinct streets that meet or intersect at a particular junction or location.
- 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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90346825c8190ab4482a1fc8eed56 |
completed | April 10, 2026, 2:03 p.m. |
| PD | Predicate disambiguation | batch_69d8bb3e48e08190b2fee43af4f57323 |
completed | April 10, 2026, 8:56 a.m. |
Created at: April 8, 2026, 9:45 p.m.