Triple
T32966249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Girl (Knemon’s daughter) |
E843377
|
entity |
| Predicate | hasNoPersonalNameInText |
P134266
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Girl (Knemon’s daughter), hasNoPersonalNameInText, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNoPersonalNameInText Context triple: [Girl (Knemon’s daughter), hasNoPersonalNameInText, true]
-
A.
hasNoPersonalNameIn
chosen
Indicates that an entity lacks a specific personal name within a given context, language, or naming system.
-
B.
isPersonalName
Indicates that the value is a personal name identifying an individual person.
-
C.
hasNoCharactersWithNames
Indicates that the subject entity does not contain any characters for whom explicit names are specified.
-
D.
isPersonalNameIn
Indicates that a personal name appears within or is contained in a specified context, such as a document, text span, or dataset.
-
E.
hasNoText
Indicates that the referenced entity or element contains no textual content.
- 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_69f3494af2808190ad98cec2f1bc0fe6 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a016cc5abcc8190b64094c777b40f6f |
completed | May 11, 2026, 5:44 a.m. |
| PD | Predicate disambiguation | batch_6a016b997ecc8190818207c57b9cbf17 |
completed | May 11, 2026, 5:39 a.m. |
Created at: May 1, 2026, 1:21 a.m.