Triple
T1983155
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sainte-Mère-Église |
E43074
|
entity |
| Predicate | distanceToUtahBeach |
P35774
|
FINISHED |
| Object | approximately 10 km |
—
|
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: approximately 10 km | Statement: [Sainte-Mère-Église, distanceToUtahBeach, approximately 10 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToUtahBeach Context triple: [Sainte-Mère-Église, distanceToUtahBeach, approximately 10 km]
-
A.
distanceToFrance
Indicates the spatial distance between a given entity and the country of France.
-
B.
numberOfChannelCrossingsAtNormandy
Indicates the number of times an entity crossed the English Channel specifically in relation to the Normandy area or campaign.
-
C.
distanceToSaintHelena
Indicates the measured distance between a given entity and the location of Saint Helena.
-
D.
distanceFromCharlotteAmalie
Indicates the measured distance between a given location and Charlotte Amalie.
-
E.
distanceFromDover
Indicates the measured distance between a given entity or location and the place named Dover.
- 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_69a88713ddc88190a969715658ebe7a8 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb96f932881908bebfc4176fda7c0 |
completed | March 7, 2026, 5:36 a.m. |
| PD | Predicate disambiguation | batch_69abb798d288819083132cf14605bd02 |
completed | March 7, 2026, 5:28 a.m. |
| PDg | Predicate description generation | batch_69abb96e07c08190beed60096e9d71b4 |
completed | March 7, 2026, 5:36 a.m. |
Created at: March 4, 2026, 7:37 p.m.