Triple
T38130186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mexican Federal Highway |
E952197
|
entity |
| Predicate | includesFreeRoads |
P72697
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Mexican Federal Highway, includesFreeRoads, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesFreeRoads Context triple: [Mexican Federal Highway, includesFreeRoads, yes]
-
A.
hasFreeRoads
chosen
Indicates that an entity provides or is associated with roads that can be used without paying tolls or fees.
-
B.
hasRoads
Indicates that there exist constructed road connections linking the related entities.
-
C.
hasNumberOfSlipRoads
Indicates the number of slip roads (on/off ramps) associated with a particular road segment or junction.
-
D.
isFreeway
Indicates that a given road segment functions as a freeway, typically designed for high-speed, limited-access vehicular traffic.
-
E.
hasFreewaySegments
Indicates that one entity includes, contains, or is associated with specific freeway segments as part of its structure or network.
- 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_69f76f083548819082bd2bbf53c79e8e |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037df1223c8190a5d61e4f8e6fd613 |
completed | May 12, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_6a037a1ad6c48190bfe35d350c1b4751 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:21 p.m.