Triple
T17688701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Piper |
E440963
|
entity |
| Predicate | hasLetterPattern |
P128581
|
FINISHED |
| Object | repeated initial P sound |
—
|
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: repeated initial P sound | Statement: [Peter Piper, hasLetterPattern, repeated initial P sound]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLetterPattern Context triple: [Peter Piper, hasLetterPattern, repeated initial P sound]
-
A.
hasLetter
Indicates that one entity contains, includes, or is associated with a specific letter or character.
-
B.
hasLetterBy
Indicates that an entity possesses or is associated with a letter authored or sent by another entity.
-
C.
hasLetterCount
Indicates that an entity is associated with a specific number representing how many letters it contains.
-
D.
hasLettersFor
Indicates that one entity possesses or contains written correspondence intended for another entity.
-
E.
hasLetterValue
Indicates that a particular letter or character is associated with a specific numeric or symbolic value.
- 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4704944d8819089b153aa14839fc0 |
completed | April 19, 2026, 6:03 a.m. |
| PD | Predicate disambiguation | batch_69e3cde3673c8190a889e14ba1f07dc1 |
completed | April 18, 2026, 6:30 p.m. |
| PDg | Predicate description generation | batch_69e3cfaac2b881909e1140339eb1a0dd |
completed | April 18, 2026, 6:38 p.m. |
Created at: April 10, 2026, 10:03 a.m.