Triple
T20104596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ida Straus |
E486963
|
entity |
| Predicate | passengerClassOnTitanic |
P50616
|
FINISHED |
| Object | First class |
—
|
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: First class | Statement: [Ida Straus, passengerClassOnTitanic, First class]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengerClassOnTitanic Context triple: [Ida Straus, passengerClassOnTitanic, First class]
-
A.
seatClass
chosen
Indicates the travel or seating category assigned to a passenger or seat (e.g., economy, business, first class).
-
B.
servesCabinClass
Indicates that a service provider (such as an airline or flight) offers or is available to a specified cabin class (e.g., economy, business, first).
-
C.
airlineClass
Indicates the specific travel class or service level assigned to a passenger or ticket on an airline flight.
-
D.
hasCabinClass
Indicates that an entity (such as a booking, ticket, or seat) is associated with a specific cabin class (e.g., economy, business, first).
-
E.
typicalEmbarkedForce
Indicates the usual or characteristic military force that is boarded onto a vehicle, vessel, or transport platform for an operation or mission.
- 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_69da62636cc08190982cc71733a17b8d |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e666daf73c819089f02ca6faa2c283 |
completed | April 20, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69e54cf788188190a46cc49c9ce7617f |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 11:27 p.m.