Triple
T34190076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Massachusetts Route 136 |
E877077
|
entity |
| Predicate | runsThroughMunicipality |
P42402
|
FINISHED |
| Object | Swansea, Massachusetts |
—
|
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: Swansea, Massachusetts | Statement: [Massachusetts Route 136, runsThroughMunicipality, Swansea, Massachusetts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: runsThroughMunicipality Context triple: [Massachusetts Route 136, runsThroughMunicipality, Swansea, Massachusetts]
-
A.
hasMunicipalityAlongCourse
Indicates that a watercourse passes through or alongside a given municipality along its path.
-
B.
flowsThroughTown
chosen
Indicates that something, typically a river or stream, passes through the area or boundaries of a town.
-
C.
spansMunicipality
Indicates that something (such as an area, structure, or feature) extends across or covers more than one municipality.
-
D.
isInMunicipality
Indicates that one entity (typically a place or address) is located within the administrative boundaries of a specific municipality.
-
E.
oftenIncludesMunicipality
Indicates that one entity frequently or typically contains or encompasses a municipality within its boundaries or scope.
- 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_69f349af20a4819089ac24d28f2d8112 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f710240640819087919759ec52e4bd |
completed | May 3, 2026, 9:06 a.m. |
| PD | Predicate disambiguation | batch_69f70f3c5bfc81908585f52e196dafe5 |
completed | May 3, 2026, 9:02 a.m. |
Created at: May 1, 2026, 1:55 a.m.