Triple
T3965884
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Normandy American Cemetery and Memorial |
E92215
|
entity |
| Predicate | numberOfUnknowns |
P53654
|
FINISHED |
| Object | over 300 |
—
|
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: over 300 | Statement: [Normandy American Cemetery and Memorial, numberOfUnknowns, over 300]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfUnknowns Context triple: [Normandy American Cemetery and Memorial, numberOfUnknowns, over 300]
-
A.
numberOfEquations
Indicates the total count of equations associated with or involved in a given entity or context.
-
B.
numberOfIndependentEquations
Indicates the count of distinct, non-redundant equations that independently constrain or relate the variables in a system.
-
C.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
D.
numberOfTruths
Indicates the quantity of statements or propositions that are true within a given context or set.
-
E.
numberOfKnownMembers
Indicates the count of members within a group or set whose identities are known or have been explicitly determined.
- 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_69aed96624188190ac8c45bb57ab72b5 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefba878a48190a2e234d775215938 |
completed | March 9, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69aef8efcf3c81908ccf61d9ce26b0c0 |
completed | March 9, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69aefba6b9848190a7b0fb66100a2a0c |
completed | March 9, 2026, 4:56 p.m. |
Created at: March 9, 2026, 3:32 p.m.