Triple
T33506913
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sex on the Moon |
E858135
|
entity |
| Predicate | hasSubjectObject |
P199729
|
FINISHED |
| Object | lunar samples |
—
|
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: lunar samples | Statement: [Sex on the Moon, hasSubjectObject, lunar samples]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubjectObject Context triple: [Sex on the Moon, hasSubjectObject, lunar samples]
-
A.
hasSubjectPosition
Indicates that an entity occupies or is assigned to a particular subject role or position within a structure, context, or organization.
-
B.
hasHumanSubject
Indicates that an entity serves as the human participant or subject involved in an action, event, or relation.
-
C.
hasSubjectRoleIn
Indicates that an entity serves in a specific subject role within a particular context, situation, or relationship.
-
D.
hasSubjectState
Indicates that an entity (typically an event, statement, or situation) is associated with a particular state or condition of its subject.
-
E.
hasSubjectObjectMarking
Indicates that a linguistic construction explicitly marks both the subject and the object, typically through morphology or syntax, to signal their grammatical roles in the clause.
- 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_69f3497721848190978fbee5e0a526f8 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff5285ed74819097e6e2a9084a079a |
completed | May 9, 2026, 3:28 p.m. |
| PD | Predicate disambiguation | batch_69ff51fbe28881908ac8417dff9db81a |
completed | May 9, 2026, 3:25 p.m. |
| PDg | Predicate description generation | batch_69ff528536d481909caefe52fcd8aaa7 |
completed | May 9, 2026, 3:28 p.m. |
Created at: May 1, 2026, 1:38 a.m.