Triple
T4033359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malaysia Airlines Flight 17 |
E83765
|
entity |
| Predicate | numberOfVictimsFromBelgium |
P53568
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Malaysia Airlines Flight 17, numberOfVictimsFromBelgium, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfVictimsFromBelgium Context triple: [Malaysia Airlines Flight 17, numberOfVictimsFromBelgium, 4]
-
A.
numberOfGermanVictims
Indicates the quantity of victims who are identified as German in the context of the described event or situation.
-
B.
mainVictims
Indicates that the related entities are the primary or principal targets harmed or affected by an action, event, or perpetrator.
-
C.
dutchCasualties
Indicates that the relationship specifies the number or occurrence of casualties suffered by Dutch entities in a given event or context.
-
D.
notableVictims
Indicates that the object is a person or group who is especially well-known or significant as a victim of the subject.
-
E.
numberOfSuspectedVictims
Indicates the count of individuals believed or alleged to be victims in a particular incident, case, or context.
- 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_69aed92e29ac819080f7a98b594fec05 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb108fc0819080c8f41da2e558e0 |
completed | March 9, 2026, 4:53 p.m. |
| PD | Predicate disambiguation | batch_69aef8fe440c819093a7fa22c4ff3f1a |
completed | March 9, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69aefa815f2c8190818c9ffd9d1bf478 |
completed | March 9, 2026, 4:51 p.m. |
Created at: March 9, 2026, 3:36 p.m.