Triple
T30913694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess of Saudi Arabia |
E787522
|
entity |
| Predicate | associatedWithTravel |
P207319
|
FINISHED |
| Object | diplomatic and official travel |
—
|
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: diplomatic and official travel | Statement: [Princess of Saudi Arabia, associatedWithTravel, diplomatic and official travel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithTravel Context triple: [Princess of Saudi Arabia, associatedWithTravel, diplomatic and official travel]
-
A.
involvedTravelBetween
Indicates a relationship where an entity participates in or is associated with travel occurring between two specified locations.
-
B.
associatedWithFlightsAt
Indicates a relationship where something is connected or linked to specific flights occurring at a particular location or time.
-
C.
associatedWithFlight
Indicates a relationship where an entity is linked or connected to a specific flight, such as by participation, operation, or relevance.
-
D.
journeyedWith
Indicates that one entity traveled or went on a journey together with another entity as companions.
-
E.
basedOnTravel
Indicates that something is determined, derived, or decided according to travel-related factors, conditions, or information.
- 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_69f224be300c8190a6513ce1ee0a7026 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379e2fac0819089b522db3260028c |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7ee0388190a29faeb5cdb0950a |
completed | May 12, 2026, 7:16 p.m. |
Created at: April 29, 2026, 8:51 p.m.