Triple
T34423650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alfonsine Tables |
E883613
|
entity |
| Predicate | earliestMajorEditionPlace |
P20839
|
FINISHED |
| Object | Paris |
—
|
NE NERFINISHED |
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: Paris | Statement: [Alfonsine Tables, earliestMajorEditionPlace, Paris]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: earliestMajorEditionPlace Context triple: [Alfonsine Tables, earliestMajorEditionPlace, Paris]
-
A.
earliestMajorCenter
Indicates that the subject is the first or oldest significant hub or focal location associated with the object.
-
B.
firstCompleteEditionPlace
chosen
Indicates the place where the first complete edition of a work was published or produced.
-
C.
firstEditionHeldIn
Indicates the location where the first edition or initial occurrence of an event was held.
-
D.
firstMajorRunCity
Indicates the city where an entity’s first major run, performance, or large-scale event initially took place.
-
E.
wasFirstMajorSeaportOf
Indicates that one place served as the earliest significant seaport for another place or region.
- 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_69f349c3dd2c819092cc9e64809f4a42 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fb6fdc7eb081908ab8475efb38c430 |
completed | May 6, 2026, 4:44 p.m. |
| PD | Predicate disambiguation | batch_69fb5a986e588190b7a10892bd2ff44c |
completed | May 6, 2026, 3:13 p.m. |
Created at: May 1, 2026, 2 a.m.