Triple
T36576651
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hani Hanjour |
E902272
|
entity |
| Predicate | numberOfPeopleKilledOnFlight77 |
P33471
|
FINISHED |
| Object | 59 passengers and crew plus 5 hijackers |
—
|
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: 59 passengers and crew plus 5 hijackers | Statement: [Hani Hanjour, numberOfPeopleKilledOnFlight77, 59 passengers and crew plus 5 hijackers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPeopleKilledOnFlight77 Context triple: [Hani Hanjour, numberOfPeopleKilledOnFlight77, 59 passengers and crew plus 5 hijackers]
-
A.
fatalitiesOnboard
chosen
Indicates that the relationship specifies the number of people who died among those present on a particular vehicle or craft.
-
B.
numberOfPeopleKilledOnGround
Indicates the count of people who were killed on the ground as a result of the event or incident.
-
C.
numberOfPeopleReportedKilled
Indicates the reported count of people who have been killed in an incident or event.
-
D.
numberOfVictimsKilled
Indicates the count of victims who were killed as a result of the referenced event or action.
-
E.
passengersAtTimeOfDestruction
Indicates that the specified passengers were present on or associated with the entity (e.g., a vehicle or vessel) at the moment it was destroyed.
- 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_69f76e64d8908190868473959a250b94 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0bf4b88190bdcfae9a14b51f0a |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:11 p.m.