Triple
T10759792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Father Murphy |
E253793
|
entity |
| Predicate | hasChildCharacters |
P95841
|
FINISHED |
| Object | group of orphaned children |
—
|
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: group of orphaned children | Statement: [Father Murphy, hasChildCharacters, group of orphaned children]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasChildCharacters Context triple: [Father Murphy, hasChildCharacters, group of orphaned children]
-
A.
hasSiblingCharacters
Indicates that two characters share at least one common parent, making them siblings in the narrative or data context.
-
B.
hasParentCharacter
Indicates that one character is the parent of another character.
-
C.
hasChildrenWith
Indicates that two entities share one or more biological or adopted children together.
-
D.
has child
Indicates that one entity is the parent of another entity, which is its child.
-
E.
hasTextualCharacter
Indicates that something possesses or exhibits the qualities of written or printed text, such as letters, symbols, or characters.
- 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_69d6aa5f54f4819082d0bbcb6f8797e6 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d72ea21c5081908babc049d0330a75 |
completed | April 9, 2026, 4:44 a.m. |
| PD | Predicate disambiguation | batch_69d6f311529c819080ca5493d55d6050 |
completed | April 9, 2026, 12:30 a.m. |
| PDg | Predicate description generation | batch_69d6fa323564819097b207eb53f8a9b8 |
completed | April 9, 2026, 1 a.m. |
Created at: April 8, 2026, 9:16 p.m.