Triple
T20740157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FPM |
E509815
|
entity |
| Predicate | typicalSpecialization |
P12230
|
FINISHED |
| Object | finance |
—
|
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: finance | Statement: [FPM, typicalSpecialization, finance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSpecialization Context triple: [FPM, typicalSpecialization, finance]
-
A.
portrayedAsSpecialization
Indicates that one entity is depicted or represented as a specialized or more specific version of another entity.
-
B.
typicalIn
chosen
Indicates that something commonly occurs, appears, or is found within a given context, category, or environment.
-
C.
exportSpecialization
Indicates a relationship where one entity specializes in exporting particular goods, services, or resources to another entity or market.
-
D.
subjectSpecialization
Indicates that one subject focuses on, or has expertise in, a particular field, topic, or area of knowledge.
-
E.
uniformSpecialty
Indicates that multiple entities share the same specific specialty, expertise, or area of focus.
- 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_69e0b4c589c08190834fb5d86d0efa2b |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c20d9d4c8190a2fd87f8a33c313d |
completed | April 21, 2026, 12:17 a.m. |
| PD | Predicate disambiguation | batch_69e5c0509608819080cdbf47fcddfe36 |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 16, 2026, 12:32 p.m.