Triple
T935468
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madam |
E20184
|
entity |
| Predicate | exampleUsage |
P1259
|
FINISHED |
| Object | Excuse me, Madam, may I help you? |
—
|
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: Excuse me, Madam, may I help you? | Statement: [Madam, exampleUsage, Excuse me, Madam, may I help you?]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: exampleUsage Context triple: [Madam, exampleUsage, Excuse me, Madam, may I help you?]
-
A.
toolUseExamples
Indicates that one entity provides example instances or demonstrations of how a particular tool is or can be used by another entity.
-
B.
usagePattern
Indicates how something is typically used or the recurring manner in which it is employed or consumed.
-
C.
usageType
Indicates the specific manner, purpose, or context in which something is used or intended to be used.
-
D.
hasExample
chosen
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
-
E.
actualUse
Indicates that an entity is currently being used or utilized in practice, as opposed to being merely available, planned, or potential.
- 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_69a493af3dc48190adb7263e6e445ea1 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b36558588190a2a9c710073624d1 |
completed | March 1, 2026, 9:45 p.m. |
| PD | Predicate disambiguation | batch_69a4b29b245c8190b143f28b77fede3c |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.