Triple
T3365691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Washington and Lee University |
E70829
|
entity |
| Predicate | isHistoricCampus |
P47590
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Washington and Lee University, isHistoricCampus, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isHistoricCampus Context triple: [Washington and Lee University, isHistoricCampus, true]
-
A.
hasCampusOn
Indicates that an institution or organization maintains a campus located on a specified geographic area or site.
-
B.
isPartOfCampus
Indicates that one place, facility, or area is located within and belongs to a larger campus.
-
C.
hasMainCampus
Indicates that an educational institution is primarily based at or chiefly associated with a particular campus location.
-
D.
hasCollegeCampus
Indicates that an institution or organization possesses or is associated with a specific college campus as a physical or organizational site.
-
E.
cityCampus
Indicates that a campus is located within or associated with a particular city.
- 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_69ad85a729d48190afd789cd8417f289 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb28643f48190b78b0222f8323344 |
completed | March 8, 2026, 5:31 p.m. |
| PD | Predicate disambiguation | batch_69ada4317e288190ab7d0f66e9dba65f |
completed | March 8, 2026, 4:30 p.m. |
| PDg | Predicate description generation | batch_69ada698eeb48190a1f5762fdd3b7b63 |
completed | March 8, 2026, 4:40 p.m. |
Created at: March 8, 2026, 3:13 p.m.