Triple
T20445044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sue (A Boy Named Sue) |
E501495
|
entity |
| Predicate | learnsFromFather |
P140139
|
FINISHED |
| Object | name was intended to make him tough |
—
|
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: name was intended to make him tough | Statement: [Sue (A Boy Named Sue), learnsFromFather, name was intended to make him tough]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: learnsFromFather Context triple: [Sue (A Boy Named Sue), learnsFromFather, name was intended to make him tough]
-
A.
fatherWas
Indicates that one entity was the male parent (father) of another entity in the past.
-
B.
fatherFrom
Indicates a parental relationship where one entity is the biological or legal father of another entity.
-
C.
successorOfFather
Indicates that one entity is the successor or heir of another entity’s father.
-
D.
fatherBasedOn
Indicates a paternal relationship inferred from indirect or derived evidence rather than directly asserted.
-
E.
learnsLanguageFrom
Indicates that one entity acquires or improves knowledge of a language through instruction, exposure, or guidance provided by another entity.
- 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_69e0b4ac0a1c81908845d0f8a56abce8 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e68cfca4788190ad57ecb504f54d11 |
completed | April 20, 2026, 8:30 p.m. |
| PD | Predicate disambiguation | batch_69e5766df0008190a73c4f613c29678f |
completed | April 20, 2026, 12:42 a.m. |
| PDg | Predicate description generation | batch_69e58d766b408190a1d3698145fb6d30 |
completed | April 20, 2026, 2:20 a.m. |
Created at: April 16, 2026, 11:32 a.m.