Triple
T14779012
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Perdita |
E347340
|
entity |
| Predicate | hasChildren |
P289
|
FINISHED |
| Object | Patch |
E1082609
|
NE 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: Patch | Statement: [Perdita, hasChildren, Patch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Patch Context triple: [Perdita, hasChildren, Patch]
-
A.
Patch
Patch is a surname most notably associated with Alexander Patch, a senior U.S. Army general who played a key role in World War II operations in Europe.
-
B.
Patch
chosen
Patch is one of the Dalmatian puppies from Disney's "101 Dalmatians," recognizable by his distinctive black ear and energetic, adventurous personality.
-
C.
Patching
Patching is a small rural village and civil parish in West Sussex, England, situated within the South Downs and known for its scenic countryside and historic church.
-
D.
Fix
Fix is a surname most notably associated with American character actor Paul Fix, known for his extensive work in Western films and television.
-
E.
Fixem
Fixem is a small commune in northeastern France, situated within the Moselle department in the Grand Est region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d822e9b9e08190bedcc31a163fda82 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deca9c7cac8190ba900df95e42e318 |
completed | April 14, 2026, 11:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe0d00d46c8190b6289a9b8511a8fb |
completed | May 8, 2026, 4:19 p.m. |
Created at: April 10, 2026, 1:31 a.m.