Triple
T147027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Route 2 (Massachusetts) |
E3351
|
entity |
| Predicate | hasFreewaySection |
P1777
|
FINISHED |
| Object | Cambridge to Acton |
—
|
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: Cambridge to Acton | Statement: [Route 2 (Massachusetts), hasFreewaySection, Cambridge to Acton]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFreewaySection Context triple: [Route 2 (Massachusetts), hasFreewaySection, Cambridge to Acton]
-
A.
hasMajorHighway
Indicates that a location or area is served by or directly connected to a major highway route.
-
B.
hasTollSegment
Indicates that a route, road, or path includes a segment where a toll must be paid.
-
C.
hasLanes
Indicates that an entity, such as a road or pathway, is divided into one or more distinct lanes for traffic or movement.
-
D.
hasApproachRoad
Indicates that one entity is connected to or accessed by another entity via an approach road leading to it.
-
E.
roadFeature
chosen
Indicates that an entity is a specific physical or functional characteristic associated with a road, such as its structure, markings, or related infrastructure.
- 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_69a252868de4819080e21c9938bfe8b6 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a258808ff08190a06b6206f635612b |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a256580c2c8190beecca60ca8595f3 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.