Triple
T3695483
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Menlo School |
E78446
|
entity |
| Predicate | offersPreparationFor |
P49795
|
FINISHED |
| Object | college admission |
—
|
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 admission | Statement: [Menlo School, offersPreparationFor, college admission]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: offersPreparationFor Context triple: [Menlo School, offersPreparationFor, college admission]
-
A.
offersServiceTo
Indicates that one entity provides or makes a service available for the benefit or use of another entity.
-
B.
offersProcess
Indicates that one entity provides or makes available a particular process for use by another entity.
-
C.
offering
Indicates that one entity presents or provides something to another entity, typically as a gift, contribution, or proposal.
-
D.
offersMeal
Indicates that one entity provides or makes available a meal to another entity.
-
E.
offersFeature
Indicates that one entity provides or makes available a particular feature or capability to another entity.
- 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_69ad85e3b1888190abc983e06968696d |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc4eafd348190986f69aee787fd8f |
completed | March 8, 2026, 6:50 p.m. |
| PD | Predicate disambiguation | batch_69adb84dc5808190850aa6975cb09e27 |
completed | March 8, 2026, 5:56 p.m. |
| PDg | Predicate description generation | batch_69adb902e61c81908f10494f828e260f |
completed | March 8, 2026, 5:59 p.m. |
Created at: March 8, 2026, 3:26 p.m.