Triple
T17238793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | General Slocum steamship disaster |
E418432
|
entity |
| Predicate | passengerGroup |
P99024
|
FINISHED |
| Object | church picnic excursion |
—
|
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: church picnic excursion | Statement: [General Slocum steamship disaster, passengerGroup, church picnic excursion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengerGroup Context triple: [General Slocum steamship disaster, passengerGroup, church picnic excursion]
-
A.
primaryPassengerGroup
chosen
Indicates the main group of passengers that is most directly associated with or served by a given entity or context.
-
B.
passengers
Indicates that one entity is traveling in or being transported by another entity, typically as a non-operating occupant.
-
C.
passengerCount
Indicates the number of passengers associated with a given entity, such as a vehicle or trip.
-
D.
hasPassengerRole
Indicates that an entity participates in a context or event specifically in the capacity or role of a passenger.
-
E.
passengerSystem
Indicates a relationship where an entity functions as or belongs to a passenger-related system (such as a transport or service system designed for passengers).
- 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_69d886d8e96081909870bff6c3d0bf09 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42dfdc8688190aceb223c19a48781 |
completed | April 19, 2026, 1:21 a.m. |
| PD | Predicate disambiguation | batch_69e3832553ac819091aa917c84f755b6 |
completed | April 18, 2026, 1:12 p.m. |
Created at: April 10, 2026, 5:39 a.m.