Triple
T1718889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Task Manager |
E37347
|
entity |
| Predicate | hasTab |
P31930
|
FINISHED |
| Object | Processes |
—
|
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: Processes | Statement: [Task Manager, hasTab, Processes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTab Context triple: [Task Manager, hasTab, Processes]
-
A.
hasTarget
Indicates that one entity is directed toward, aimed at, or intended to affect another specific entity as its target.
-
B.
hasPar
Indicates a relationship where one entity has another entity as its parent.
-
C.
hasTableService
Indicates that a place or establishment provides table service, where staff serve customers at their tables rather than requiring self-service.
-
D.
hasCurrent
Indicates that an entity presently possesses, exhibits, or is associated with a particular state, attribute, or resource at the current time.
-
E.
hasPad
Indicates that one entity possesses, is equipped with, or is associated with a pad (such as a cushion, tablet, or protective surface).
- F. None of above. chosen
Provenance (4 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_69a8861912dc8190931af43b4b9158a7 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69ab5c96db6c8190a745d6fef7bf2cdb |
completed | March 6, 2026, 11 p.m. |
| PD | Predicate disambiguation | batch_69aa61bed2fc819086d912cd34285978 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab5c7780bc81909fc6e173a216cb0b |
completed | March 6, 2026, 11 p.m. |
Created at: March 4, 2026, 7:30 p.m.