Triple
T19244324
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scout.com |
E481208
|
entity |
| Predicate | coveredLevel |
P29174
|
FINISHED |
| Object | college |
—
|
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: college | Statement: [Scout.com, coveredLevel, college]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coveredLevel Context triple: [Scout.com, coveredLevel, college]
-
A.
coversLevel
chosen
Indicates that one entity includes or encompasses a particular level or layer of another entity or system.
-
B.
representedLevel
Indicates that one entity denotes or encodes the degree, intensity, or value (i.e., the level) of another entity or property.
-
C.
covered
Indicates that one entity lies over or on top of another entity so as to conceal, protect, or obscure it.
-
D.
contentLevel
Indicates the degree or intensity of substance, complexity, or richness present in the associated content.
-
E.
representationLevel
Indicates the degree or layer at which something stands in for, models, or symbolizes something else (e.g., more concrete vs. more abstract representation).
- 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_69d8e8cd9d1081908a181d02b88b59b8 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5faf47820819081e8b6af852bb1dd |
completed | April 20, 2026, 10:07 a.m. |
| PD | Predicate disambiguation | batch_69e4dd002d00819088b625056edfb74e |
completed | April 19, 2026, 1:47 p.m. |
Created at: April 10, 2026, 1:27 p.m.