Triple
T33549037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huffman Aviation |
E859284
|
entity |
| Predicate | trainedPerson |
P41095
|
FINISHED |
| Object | Marwan al-Shehhi |
—
|
NE NERFINISHED |
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: Marwan al-Shehhi | Statement: [Huffman Aviation, trainedPerson, Marwan al-Shehhi]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainedPerson Context triple: [Huffman Aviation, trainedPerson, Marwan al-Shehhi]
-
A.
trainedAs
Indicates that one entity has received education or instruction to perform the role, profession, or function represented by another entity.
-
B.
trainer
chosen
Indicates a relationship where one entity teaches, coaches, or prepares another entity to develop skills, knowledge, or performance in a particular domain.
-
C.
trainedAccordingTo
Indicates that an entity has been trained or instructed in alignment with a specified method, standard, guideline, or curriculum.
-
D.
trainedNear
Indicates that one entity received training at a location that is geographically close to another specified entity or location.
-
E.
physicallyConditionedAs
Indicates that one entity has been brought into or exists in a particular physical state or condition as specified by another entity or descriptor.
- 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_69f3497a5be08190a39b12736899e034 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f71fb1ab3881908e2f7c0e6f23db49 |
completed | May 3, 2026, 10:13 a.m. |
| PD | Predicate disambiguation | batch_69f71cc6397881909aaad37a9daa8a7e |
completed | May 3, 2026, 10 a.m. |
Created at: May 1, 2026, 1:39 a.m.