Triple
T295752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Connecticut Yankee in King Arthur's Court |
E6088
|
entity |
| Predicate | timeTravelFrom |
P10439
|
FINISHED |
| Object | 19th-century United States |
—
|
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: 19th-century United States | Statement: [A Connecticut Yankee in King Arthur's Court, timeTravelFrom, 19th-century United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeTravelFrom Context triple: [A Connecticut Yankee in King Arthur's Court, timeTravelFrom, 19th-century United States]
-
A.
movementDate
Indicates the date on which a movement, transfer, or relocation event occurs.
-
B.
time
Indicates a temporal relationship specifying when an event occurs or how entities are ordered or related in time.
-
C.
timePeriod
Indicates the specific span or interval of time during which an event, state, or relationship occurs or is valid.
-
D.
timeDescribedAs
Indicates that a specific time or temporal interval is characterized, labeled, or expressed using a particular description or representation.
-
E.
involvedTravelBetween
Indicates a relationship where an entity participates in or is associated with travel occurring between two specified locations.
- 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_69a2e79114b081909490b3bf5a5dbb51 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2e9e273f88190ac5355d1310376ed |
completed | Feb. 28, 2026, 1:13 p.m. |
| PD | Predicate disambiguation | batch_69a2e9368894819093eeae4347dfcc5a |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2e9e0d85c8190ae52662d83ea67fe |
completed | Feb. 28, 2026, 1:13 p.m. |
Created at: Feb. 28, 2026, 1:06 p.m.