Triple
T2125693
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Enterprise Hub |
E46419
|
entity |
| Predicate | programmeType |
P2192
|
FINISHED |
| Object | accelerator |
—
|
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: accelerator | Statement: [Enterprise Hub, programmeType, accelerator]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: programmeType Context triple: [Enterprise Hub, programmeType, accelerator]
-
A.
programType
chosen
Indicates the category or kind of program to which an entity belongs or with which it is associated.
-
B.
programmeName
Indicates that an entity has or is identified by a specific programme’s name.
-
C.
notableProgramType
Indicates that the subject is recognized for or associated with a particular type or category of program.
-
D.
hasProgramme
Indicates that an entity is associated with or offers a particular programme (such as a course of study, plan, or structured set of activities).
-
E.
theatreType
Indicates the specific category or kind of theatre associated with an entity, such as its format, style, or operational model.
- 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_69a88a1626548190ae59a5028c3baa8e |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abbb57bc6881909f04a407beff33a6 |
completed | March 7, 2026, 5:44 a.m. |
| PD | Predicate disambiguation | batch_69abb7bd86cc8190938ef06c1ed6d969 |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:44 p.m.