Triple
T11176261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LSST Camera |
E264420
|
entity |
| Predicate | supportsScienceGoal |
P7242
|
FINISHED |
| Object | dark energy studies |
—
|
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: dark energy studies | Statement: [LSST Camera, supportsScienceGoal, dark energy studies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsScienceGoal Context triple: [LSST Camera, supportsScienceGoal, dark energy studies]
-
A.
scienceGoals
chosen
Indicates the objectives or intended outcomes that guide and justify a scientific activity, project, or investigation.
-
B.
supportsPolicyGoal
Indicates that one entity’s actions, positions, or characteristics help advance, uphold, or contribute to achieving a specified policy goal.
-
C.
hasScience
Indicates that an entity possesses, includes, or is associated with a particular scientific discipline, content, or attribute.
-
D.
supportGoal
Indicates that one entity actively helps, promotes, or contributes to the achievement of another entity’s goal.
-
E.
supportsResearchIn
Indicates that one entity provides resources, assistance, or infrastructure that enables or advances research activities in a particular field or area for 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_69d6aa9dafac8190bd90d2c74f661aa7 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8987e1081909b28a0bdb866beae |
completed | April 9, 2026, 5:57 p.m. |
| PD | Predicate disambiguation | batch_69d75cf0e6e88190973694abe2990973 |
completed | April 9, 2026, 8:01 a.m. |
Created at: April 8, 2026, 9:29 p.m.