Triple
T5301228
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franco-German border |
E119984
|
entity |
| Predicate | hasSymbolicRoleIn |
P60351
|
FINISHED |
| Object | Franco-German reconciliation |
—
|
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: Franco-German reconciliation | Statement: [Franco-German border, hasSymbolicRoleIn, Franco-German reconciliation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSymbolicRoleIn Context triple: [Franco-German border, hasSymbolicRoleIn, Franco-German reconciliation]
-
A.
symbolicRoleOfTarget
chosen
Indicates that the target entity serves as a symbolic representation or emblem for another entity or concept.
-
B.
hasNotableRoleIn
Indicates that an entity holds a significant or noteworthy role or function within another entity, event, work, or context.
-
C.
hasCapitalRole
Indicates that an entity holds an official role, function, or status specifically associated with a capital city.
-
D.
hasRelativeRole
Indicates that one entity holds a familial or kinship-based role in relation to another entity.
-
E.
hasAuxiliaryRole
Indicates that an entity serves in a supporting or secondary capacity to another entity or primary role.
- 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_69bd44704be88190acdb2ac481b0ff55 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd8e44e7c881909b241b2fec366038 |
completed | March 20, 2026, 6:13 p.m. |
| PD | Predicate disambiguation | batch_69bd845097ac81909678624c4907fda4 |
completed | March 20, 2026, 5:30 p.m. |
Created at: March 20, 2026, 1:53 p.m.