Triple
T15372114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fred Haise |
E367572
|
entity |
| Predicate | wasTrainedFor |
P54797
|
FINISHED |
| Object | Apollo 19 commander |
—
|
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: Apollo 19 commander | Statement: [Fred Haise, wasTrainedFor, Apollo 19 commander]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wasTrainedFor Context triple: [Fred Haise, wasTrainedFor, Apollo 19 commander]
-
A.
trainedAs
Indicates that one entity has received education or instruction to perform the role, profession, or function represented by another entity.
-
B.
providesTrainingFor
Indicates that one entity delivers or conducts training activities intended to develop the skills or knowledge of another entity.
-
C.
hasTrained
Indicates that one entity has provided training or instruction to another entity.
-
D.
hasTrainingFor
chosen
Indicates that an entity has received or possesses training that prepares it for performing a specific task, role, or function.
-
E.
receivedTrainingIn
Indicates that one entity has undergone or been provided with training or instruction in a particular field, skill, or subject associated with 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e5c1d548190930bfaf0861595ae |
completed | April 16, 2026, 1:41 a.m. |
| PD | Predicate disambiguation | batch_69ded27742a881909cd73cc5c7d062fd |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:18 a.m.