Triple
T10360453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marwan al-Shehhi |
E244118
|
entity |
| Predicate | positionOnFlight |
P93860
|
FINISHED |
| Object | pilot hijacker of United Airlines Flight 175 |
—
|
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: pilot hijacker of United Airlines Flight 175 | Statement: [Marwan al-Shehhi, positionOnFlight, pilot hijacker of United Airlines Flight 175]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: positionOnFlight Context triple: [Marwan al-Shehhi, positionOnFlight, pilot hijacker of United Airlines Flight 175]
-
A.
locationOnAircraft
Indicates that one entity is physically situated on or within an aircraft.
-
B.
seatOn
Indicates that one entity is positioned or placed on a seat or seating surface associated with another entity.
-
C.
positionOn
Indicates that one entity is located on top of or at a specific place along the surface or extent of another entity.
-
D.
typicalPilotPosition
Indicates the usual or standard spatial position or placement where a pilot is located relative to the associated object or system.
-
E.
hasRelativePositionAtAirport
Indicates that one entity has a specific spatial or positional relationship to another entity within the context or layout of an airport.
- 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_69d381b22b8c8190aaed476be5f872a9 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9609c4481908b7d72ecf1adaa73 |
completed | April 7, 2026, 11:24 a.m. |
| PD | Predicate disambiguation | batch_69d4dfa657f481909cc5cc8fec00ad19 |
completed | April 7, 2026, 10:42 a.m. |
| PDg | Predicate description generation | batch_69d4e91ce2008190af252c140370b7f2 |
completed | April 7, 2026, 11:23 a.m. |
Created at: April 6, 2026, 11:59 a.m.