Triple
T28996540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Age (HTTP header) |
E736180
|
entity |
| Predicate | hasExampleValue |
P18302
|
FINISHED |
| Object | Age: 0 |
—
|
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: Age: 0 | Statement: [Age (HTTP header), hasExampleValue, Age: 0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasExampleValue Context triple: [Age (HTTP header), hasExampleValue, Age: 0]
-
A.
hasExample
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
-
B.
hasExampleType
Indicates that something is associated with a specific type or category of example that characterizes or illustrates it.
-
C.
hasExampleProvider
Indicates that one entity serves as an example provider or source of illustrative instances for another entity.
-
D.
hasValue
chosen
Indicates that an entity is associated with a specific numerical, textual, or otherwise defined value.
-
E.
hasExampleImplementation
Indicates that an entity is accompanied by a concrete implementation that serves as an example of how it can be realized or used.
- 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_69f077eacd0481908ef0bafd74491cd0 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69f6db1f3ec48190a82e7d893d3c76ba |
completed | May 3, 2026, 5:20 a.m. |
| PD | Predicate disambiguation | batch_69f6d82adfa481908a5e196d2e18c73f |
completed | May 3, 2026, 5:07 a.m. |
Created at: April 28, 2026, 9:31 a.m.