Triple
T6745292
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Parker Harris |
E154194
|
entity |
| Predicate | hasBusinessModelFocus |
P21224
|
FINISHED |
| Object | subscription software |
—
|
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: subscription software | Statement: [Parker Harris, hasBusinessModelFocus, subscription software]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBusinessModelFocus Context triple: [Parker Harris, hasBusinessModelFocus, subscription software]
-
A.
businessModelFocus
chosen
Indicates that one entity’s business model is centered on, tailored to, or primarily oriented around another entity or specific focus area.
-
B.
hasBusinessModelCharacteristic
Indicates that a business model possesses or exhibits a specific feature, quality, or attribute.
-
C.
usesBusinessModel
Indicates that one entity operates according to, or applies in practice, the business model defined or provided by another entity.
-
D.
hasMarketFocus
Indicates that an entity concentrates its business activities, products, or services on a particular market or customer segment.
-
E.
hasEconomicFocus
Indicates that an entity is primarily concerned with, oriented toward, or specializing in economic matters, activities, or impacts.
- 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_69c6880ef37881909268a5a7299b9293 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d1b74ae081908575c4e47c0ef297 |
completed | March 27, 2026, 6:51 p.m. |
| PD | Predicate disambiguation | batch_69c6d09067a0819087ed6c820f4699f8 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:10 p.m.