Triple
T8898099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quaker City |
E211854
|
entity |
| Predicate | hasNotablePassengerRole |
P27764
|
FINISHED |
| Object | Mark Twain as travel correspondent |
—
|
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: Mark Twain as travel correspondent | Statement: [Quaker City, hasNotablePassengerRole, Mark Twain as travel correspondent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotablePassengerRole Context triple: [Quaker City, hasNotablePassengerRole, Mark Twain as travel correspondent]
-
A.
hasPassengerRole
Indicates that an entity participates in a context or event specifically in the capacity or role of a passenger.
-
B.
hasNotableTraveler
Indicates that an entity is associated with a traveler who is considered notable or significant in some recognized way.
-
C.
hasNotableRoleIn
chosen
Indicates that an entity holds a significant or noteworthy role or function within another entity, event, work, or context.
-
D.
hasPassengerOperator
Indicates that an entity (such as a vehicle or service) is operated by an organization or person responsible for carrying passengers.
-
E.
hasPassengerAirlineService
Indicates that a location or facility is served by scheduled passenger airline flights.
- 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_69ca83918d3081909b326fa3750cb8c8 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc642618908190b3df50cbbabff93d |
completed | April 1, 2026, 12:17 a.m. |
| PD | Predicate disambiguation | batch_69cc5c2bfb38819083d5eb1af8ccf4d6 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:54 p.m.