Triple
T12514561
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | wc |
E299162
|
entity |
| Predicate | wordDefinition |
P105366
|
FINISHED |
| Object | sequence of non-whitespace characters |
—
|
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: sequence of non-whitespace characters | Statement: [wc, wordDefinition, sequence of non-whitespace characters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wordDefinition Context triple: [wc, wordDefinition, sequence of non-whitespace characters]
-
A.
duMeaning
Indicates that one entity expresses, conveys, or signifies a particular meaning or sense in relation to another.
-
B.
tegMeaning
Indicates that one entity expresses, conveys, or stands for the meaning or semantic content of another entity.
-
C.
ermenMeaning
Indicates that one entity represents or conveys the meaning or semantic interpretation of another entity.
-
D.
letterMeaning
Indicates that a particular letter conveys a specific meaning, interpretation, or semantic content.
-
E.
textMeaning
Indicates that one text expresses, conveys, or corresponds to a particular meaning or semantic content.
- 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_69d6ada4cd388190ae3bbf83ff87057a |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d954b867dc8190af8a70f797e4d133 |
completed | April 10, 2026, 7:51 p.m. |
| PD | Predicate disambiguation | batch_69d954096af88190b6be81b008c82139 |
completed | April 10, 2026, 7:48 p.m. |
| PDg | Predicate description generation | batch_69d954b715fc819091fa84430be46273 |
completed | April 10, 2026, 7:51 p.m. |
Created at: April 8, 2026, 9:57 p.m.