Triple
T37868160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vaughn, New Mexico |
E944530
|
entity |
| Predicate | isTransportationCrossroads |
P184542
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Vaughn, New Mexico, isTransportationCrossroads, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isTransportationCrossroads Context triple: [Vaughn, New Mexico, isTransportationCrossroads, true]
-
A.
isLocalCrossroads
Indicates that a location serves as a junction where multiple local routes or streets intersect.
-
B.
hasTransportationCrossings
Indicates that one location or route includes points where transportation paths (such as roads, railways, or walkways) intersect or cross over/under another feature.
-
C.
roadCrossingBetween
Indicates that a road crossing (such as a crosswalk or intersection crossing) exists between two locations or road segments, connecting them for passage across the road.
-
D.
atJunctionOf
chosen
Indicates that something is located at or directly connected to the point where two or more paths, roads, or lines meet.
-
E.
crossesStreet
Indicates that an entity moves from one side of a street to the other, traversing the street’s width.
- 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_69f76eef55d481908ca6660b4b532550 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fc4748843c8190931432653be4890c |
completed | May 7, 2026, 8:03 a.m. |
| PD | Predicate disambiguation | batch_69fc45646ce481908caf292ff9f06e15 |
completed | May 7, 2026, 7:55 a.m. |
Created at: May 3, 2026, 4:19 p.m.