Triple
T7269
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Research Experiences for Undergraduates |
E143
|
entity |
| Predicate | benefit |
P487
|
FINISHED |
| Object | student stipend |
—
|
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: student stipend | Statement: [Research Experiences for Undergraduates, benefit, student stipend]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: benefit Context triple: [Research Experiences for Undergraduates, benefit, student stipend]
-
A.
benefits
chosen
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or action.
-
B.
benefitedCountry
Indicates that one country gains an advantage, profit, or positive outcome from an action, event, or entity associated with another.
-
C.
purpose
Indicates that one entity exists, is done, or is used in order to achieve, support, or serve the goal, function, or intended outcome of another entity.
-
D.
backing
Indicates providing support, endorsement, or financial/resources assistance to someone or something, often enabling or strengthening their actions or position.
-
E.
advises
Indicates that one entity provides guidance, recommendations, or counsel to another entity.
- 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_69a23bb612708190b09f25385e4b63d1 |
completed | Feb. 28, 2026, 12:49 a.m. |
| NER | Named-entity recognition | batch_69a241a55ac081909e95b71c97db8140 |
completed | Feb. 28, 2026, 1:15 a.m. |
| PD | Predicate disambiguation | batch_69a23fe1cf38819080ea56c40bf2632e |
completed | Feb. 28, 2026, 1:07 a.m. |
Created at: Feb. 28, 2026, 12:54 a.m.