Triple
T15697718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Douglass Campus |
E380505
|
entity |
| Predicate | hasEmphasisOn |
P17414
|
FINISHED |
| Object | women’s education |
—
|
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: women’s education | Statement: [Douglass Campus, hasEmphasisOn, women’s education]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEmphasisOn Context triple: [Douglass Campus, hasEmphasisOn, women’s education]
-
A.
hasEmphasis
chosen
Indicates that one element is given special stress, importance, or prominence relative to others.
-
B.
emphasizesConsonanceOf
Indicates that one entity highlights, stresses, or draws attention to the harmony, agreement, or consonant relationship between itself and another entity.
-
C.
hasImpactFocus
Indicates that an entity is primarily concerned with or directed toward a particular type or area of impact.
-
D.
normativelyEmphasizes
Indicates that one entity highlights, prioritizes, or stresses another entity as a standard, value, or principle that ought to be followed or given special importance.
-
E.
historicallyEmphasized
Indicates that one entity has placed notable focus, priority, or importance on another entity or concept over a historical period.
- 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_69d86d99e860819094b6957cde470f2c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b4d6b5788190883746ee82c799f5 |
completed | April 16, 2026, 10:07 a.m. |
| PD | Predicate disambiguation | batch_69e0051d639481909a10614e8f83e659 |
completed | April 15, 2026, 9:37 p.m. |
Created at: April 10, 2026, 4:44 a.m.