Triple
T31874750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Claire Smith-Wyman |
E813707
|
entity |
| Predicate | letterWrittenIn |
P11300
|
FINISHED |
| Object | 1957 |
—
|
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: 1957 | Statement: [Claire Smith-Wyman, letterWrittenIn, 1957]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: letterWrittenIn Context triple: [Claire Smith-Wyman, letterWrittenIn, 1957]
-
A.
letter3
Indicates that one entity is the third letter (or character) of another entity, typically a string or word.
-
B.
willWrittenIn
Indicates that a legal will was authored, drafted, or formally written in a specified location or jurisdiction.
-
C.
letters
Indicates that one entity is composed of, represented by, or associated with specific alphabetic characters or written symbols.
-
D.
writtenIn
Indicates that a work (such as a text, program, or document) is expressed or encoded using a particular language or notation.
-
E.
wroteIn
chosen
Indicates that an entity authored or composed something using a particular language, medium, or writing system.
- 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_69f348ecb07481909c8f72619131b115 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6b21e7e088190832a3db585daea1c |
completed | May 3, 2026, 2:25 a.m. |
| PD | Predicate disambiguation | batch_69f6b14faf608190a25b977c0740729c |
completed | May 3, 2026, 2:22 a.m. |
Created at: April 30, 2026, 11:55 p.m.