Triple
T24497372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CU |
E617829
|
entity |
| Predicate | typicalMeaning |
P156529
|
FINISHED |
| Object | main university campus |
—
|
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: main university campus | Statement: [CU, typicalMeaning, main university campus]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalMeaning Context triple: [CU, typicalMeaning, main university campus]
-
A.
commonMeaning
Indicates that multiple entities share the same or very similar meaning or semantic interpretation.
-
B.
basicMeaning
Indicates that one entity expresses or conveys the fundamental or core meaning of another entity.
-
C.
logicalMeaning
Indicates that one entity expresses, encodes, or conveys the logical content, implication, or formal meaning of another.
-
D.
stringMeaning
Indicates that one entity represents the semantic content or interpretation of a given string associated with another entity.
-
E.
textMeaning
Indicates that one text expresses, conveys, or corresponds to a particular meaning or semantic content.
- F. None of above. chosen
Provenance (4 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_69e2d7f682108190a1a7ca5fd485ee8a |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f2a9d912e88190bc39c05a9d7f407e |
completed | April 30, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69f2a6a4580481908fddc385f5262f95 |
completed | April 30, 2026, 12:47 a.m. |
| PDg | Predicate description generation | batch_69f2a9d795288190916368e3cec1f666 |
completed | April 30, 2026, 1:01 a.m. |
Created at: April 18, 2026, 2:22 a.m.