Triple
T6258329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rolla, Missouri, United States |
E140229
|
entity |
| Predicate | hasUniversitySpecialization |
P466
|
FINISHED |
| Object | science |
—
|
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: science | Statement: [Rolla, Missouri, United States, hasUniversitySpecialization, science]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUniversitySpecialization Context triple: [Rolla, Missouri, United States, hasUniversitySpecialization, science]
-
A.
isSpecializedCampus
Indicates that a campus is dedicated to a specific field, discipline, or set of specialized programs rather than offering a broad, general curriculum.
-
B.
hasTechnicalUniversity
Indicates that a place or organization possesses or hosts a technical university as part of its institutions or infrastructure.
-
C.
hasUniversities
Indicates that an entity possesses, contains, or is associated with one or more universities.
-
D.
hasSpecialist
Indicates that one entity is associated with or assigned to a specialist entity that provides expert support, service, or oversight for it.
-
E.
hasSpecialty
chosen
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
- 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_69c008c95c5c819084bd3dd56133d84d |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c06367e0b48190967ebfb9bfbc9732 |
completed | March 22, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69c05605566c81908e197f5accd072d2 |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:24 p.m.