Triple
T37635583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mills, Wyoming |
E936478
|
entity |
| Predicate | hasUrbanRelationshipWith |
P94117
|
FINISHED |
| Object | Casper, Wyoming |
—
|
NE NERFINISHED |
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: Casper, Wyoming | Statement: [Mills, Wyoming, hasUrbanRelationshipWith, Casper, Wyoming]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUrbanRelationshipWith Context triple: [Mills, Wyoming, hasUrbanRelationshipWith, Casper, Wyoming]
-
A.
hasUrbanRelation
chosen
Indicates a relationship where one entity is connected to another through an urban context, such as city-based location, influence, or interaction.
-
B.
haveRelationshipWith
Indicates that one entity is in some form of defined relationship or association with another entity.
-
C.
inRelationshipWith
Indicates that two entities are mutually involved in a defined personal, romantic, or partnership relationship with each other.
-
D.
hasNeighborRelationshipWith
Indicates that one entity is located adjacent to or directly next to another entity, sharing a neighbor relationship.
-
E.
worksInCloseRelationshipWith
Indicates a collaborative professional relationship in which two or more entities work together closely and interact frequently to achieve shared goals.
- 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_69f76ed31d8881908405da6c6d2f0463 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbaa1321b48190af92a3e7ec24ec5b |
completed | May 6, 2026, 8:52 p.m. |
| PD | Predicate disambiguation | batch_69fba8860f98819080b7bab05837b974 |
completed | May 6, 2026, 8:45 p.m. |
Created at: May 3, 2026, 4:18 p.m.