Triple
T9694774
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bojinka plot |
E234619
|
entity |
| Predicate | intendedVictims |
P51429
|
FINISHED |
| Object | airline passengers |
—
|
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: airline passengers | Statement: [Bojinka plot, intendedVictims, airline passengers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: intendedVictims Context triple: [Bojinka plot, intendedVictims, airline passengers]
-
A.
involvesIntendedVictim
chosen
Indicates that an action, event, or plan is directed toward and meant to affect a specific intended victim.
-
B.
hasVictims
Indicates that an entity has one or more individuals who have been harmed, injured, or adversely affected by it.
-
C.
mainVictims
Indicates that the related entities are the primary or principal targets harmed or affected by an action, event, or perpetrator.
-
D.
victimGroup
Indicates that one group or entity is the target or recipient of harm, abuse, or wrongdoing caused by another.
-
E.
numberOfVictimsClaimed
Indicates the reported count of victims associated with a particular event, incident, or action.
- 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_69ca84cb580c8190a7e5f4b3bcdaf2a4 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9d348868819083aec7a5da8c455b |
completed | April 1, 2026, 10:33 p.m. |
| PD | Predicate disambiguation | batch_69ccd5b840f081909f66bf0b66d17d9b |
completed | April 1, 2026, 8:22 a.m. |
Created at: March 30, 2026, 8:17 p.m.