Triple
T10943431
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fayez Banihammad |
E258532
|
entity |
| Predicate | roleOnFlight |
P15253
|
FINISHED |
| Object | muscle hijacker |
—
|
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: muscle hijacker | Statement: [Fayez Banihammad, roleOnFlight, muscle hijacker]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleOnFlight Context triple: [Fayez Banihammad, roleOnFlight, muscle hijacker]
-
A.
positionOnFlight
Indicates the specific seat or positional assignment that an entity has on a particular flight.
-
B.
associatedWithFlight
Indicates a relationship where an entity is linked or connected to a specific flight, such as by participation, operation, or relevance.
-
C.
hasPassengerRole
chosen
Indicates that an entity participates in a context or event specifically in the capacity or role of a passenger.
-
D.
usedByAirlineRole
Indicates that something (such as a resource, system, or process) is utilized by a specific role or position within an airline organization.
-
E.
businessRoleOfSidLuft
Indicates that the subject holds or held a business-related role or position in relation to Sid Luft.
- 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_69d6aa8769b4819082bfe5e61b9017f0 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770c3fb388190a598f89ae59a7b51 |
completed | April 9, 2026, 9:26 a.m. |
| PD | Predicate disambiguation | batch_69d72e816a98819096d6c10dfb88a66a |
completed | April 9, 2026, 4:43 a.m. |
Created at: April 8, 2026, 9:23 p.m.