Triple
T33384983
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Five Points, Montgomeryville |
E854887
|
entity |
| Predicate | numberOfIntersectingRoads |
P65698
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [Five Points, Montgomeryville, numberOfIntersectingRoads, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfIntersectingRoads Context triple: [Five Points, Montgomeryville, numberOfIntersectingRoads, 5]
-
A.
numberOfConvergingStreets
chosen
Indicates the count of distinct streets that meet or intersect at a particular junction or location.
-
B.
numberOfRoadways
Indicates the count of distinct roadways associated with or present at a given entity or location.
-
C.
hasRoads
Indicates that there exist constructed road connections linking the related entities.
-
D.
connectsRoadNetwork
Indicates that one entity is linked to another as part of the same road network, enabling continuous vehicular or transport connectivity between them.
-
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.
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_69f3496d54048190a1cb91fdd7caa6ea |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a0316136a14819085bb88563d5abc42 |
completed | May 12, 2026, 11:59 a.m. |
| PD | Predicate disambiguation | batch_6a03158a962c81909f17d58197f6ba00 |
completed | May 12, 2026, 11:56 a.m. |
Created at: May 1, 2026, 1:35 a.m.