Triple
T5438276
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Straßburg, Alsace-Lorraine, German Empire |
E122065
|
entity |
| Predicate | statusDuringFrancoPrussianWar |
P19322
|
FINISHED |
| Object | besieged city |
—
|
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: besieged city | Statement: [Straßburg, Alsace-Lorraine, German Empire, statusDuringFrancoPrussianWar, besieged city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: statusDuringFrancoPrussianWar Context triple: [Straßburg, Alsace-Lorraine, German Empire, statusDuringFrancoPrussianWar, besieged city]
-
A.
statusDuringWar
chosen
Indicates the role, condition, or classification an entity held specifically during a period of war.
-
B.
statusAfterFranco
Indicates the status or condition of an entity following the period or rule associated with Franco.
-
C.
nomDeGuerre
Indicates that an entity uses or is known by a pseudonymous or war-related alias instead of their real name.
-
D.
FrenchCasualties
Indicates that the relationship specifies the number or extent of casualties suffered by French forces in a given event or context.
-
E.
statusDuringWorldWarII
Indicates the role, condition, or classification an entity had specifically during the period of World War II.
- 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_69bd46400768819092925d461c0b8432 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd922f66bc8190b7d47fd68d2fcf2e |
completed | March 20, 2026, 6:30 p.m. |
| PD | Predicate disambiguation | batch_69bd919aeb048190b786f814177d6cd9 |
completed | March 20, 2026, 6:27 p.m. |
Created at: March 20, 2026, 2:07 p.m.