Triple
T4033357
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malaysia Airlines Flight 17 |
E83765
|
entity |
| Predicate | numberOfVictimsFromIndonesia |
P53567
|
FINISHED |
| Object | 12 |
—
|
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: 12 | Statement: [Malaysia Airlines Flight 17, numberOfVictimsFromIndonesia, 12]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfVictimsFromIndonesia Context triple: [Malaysia Airlines Flight 17, numberOfVictimsFromIndonesia, 12]
-
A.
numberOfSuspectedVictims
Indicates the count of individuals believed or alleged to be victims in a particular incident, case, or context.
-
B.
mainVictims
Indicates that the related entities are the primary or principal targets harmed or affected by an action, event, or perpetrator.
-
C.
estimatedVictimsUnderAuthority
Indicates that a specified authority is estimated to have a certain number of victims under its control, influence, or jurisdiction.
-
D.
nativeCasualties
Indicates that native or indigenous people suffered deaths or injuries as a result of a particular event, action, or conflict.
-
E.
casualtiesJapanKilled
Indicates that the casualties were individuals from Japan who were killed.
- 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.