Triple
T7930197
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ODOT highways |
E184169
|
entity |
| Predicate | hasMaintenanceActivities |
P71131
|
FINISHED |
| Object | pavement repair |
—
|
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: pavement repair | Statement: [ODOT highways, hasMaintenanceActivities, pavement repair]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMaintenanceActivities Context triple: [ODOT highways, hasMaintenanceActivities, pavement repair]
-
A.
hasMaintenance
chosen
Indicates that an entity is subject to, associated with, or requires a particular maintenance activity or maintenance record.
-
B.
hasMaintenanceService
Indicates that an entity receives or is covered by a maintenance service provided by another entity.
-
C.
hasActivityIn
Indicates that an entity engages in or performs a particular activity within a specified context, location, or domain.
-
D.
hasMaintenanceGoal
Indicates that an entity is associated with a specific objective or target related to its upkeep, repair, or ongoing maintenance activities.
-
E.
hasRestorationActivities
Indicates that an entity carries out, is involved in, or is associated with actions aimed at restoring or rehabilitating another entity or resource.
- 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_69ca828fe7bc819090f52c88dcd72183 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3acb6cb88190b4f31b7091881241 |
completed | March 31, 2026, 3:08 a.m. |
| PD | Predicate disambiguation | batch_69cae9335f288190ba96781fd6576a2b |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:07 p.m.