Triple
T14792509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Douaumont military cemetery |
E347690
|
entity |
| Predicate | numberOfFrenchWarDeadCommemorated |
P14905
|
FINISHED |
| Object | tens of thousands |
—
|
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: tens of thousands | Statement: [Douaumont military cemetery, numberOfFrenchWarDeadCommemorated, tens of thousands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFrenchWarDeadCommemorated Context triple: [Douaumont military cemetery, numberOfFrenchWarDeadCommemorated, tens of thousands]
-
A.
FrenchCasualties
chosen
Indicates that the relationship specifies the number or extent of casualties suffered by French forces in a given event or context.
-
B.
FrancoSpanishCasualtiesKilledAndWounded
Indicates the number of people from Franco-Spanish forces who were killed or wounded as casualties in a conflict or event.
-
C.
casualtiesFrancoBavarian
Indicates that there were casualties suffered by the Franco-Bavarian side in a particular conflict or event.
-
D.
causeOfDeathsCommemorated
Indicates that one entity is the cause of the deaths that are formally remembered or honored by another entity.
-
E.
militaryCasualtiesSide
Indicates the side or party in a conflict to which the recorded military casualties belong.
- 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_69d822ea8b7c819097dfadf3d45545e6 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69decd5ec43c8190ad7a10a556519bb0 |
completed | April 14, 2026, 11:27 p.m. |
| PD | Predicate disambiguation | batch_69de8c090d1081909b5a9bf437499d6c |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:31 a.m.