Triple
T16256816
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Betty Joan Perske |
E394650
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Harper |
E644446
|
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: Harper | Statement: [Betty Joan Perske, notableWork, Harper]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harper Context triple: [Betty Joan Perske, notableWork, Harper]
-
A.
Harper
Harper is a major American publishing house known for releasing a wide range of influential fiction and nonfiction works.
-
B.
Harper
Harper is a small community located in Raleigh County, West Virginia, in the United States.
-
C.
Harper
Harper is a common English surname of Anglo-Saxon origin, historically referring to someone who played the harp.
-
D.
Harper
chosen
"Harper" is a 1966 neo-noir mystery film, adapted by screenwriter William Goldman from Ross Macdonald’s novel "The Moving Target" and starring Paul Newman as private detective Lew Harper.
-
E.
Harper
Harper is a feminine given name of English origin that has become popular in recent years, often associated with literary and musical connotations.
- 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_69d87f221d8081909b0b2063e7528ba2 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2459b1624819086bf681075097235 |
completed | April 17, 2026, 2:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a000eebcfe481909822290d3a7b361c |
completed | May 10, 2026, 4:51 a.m. |
Created at: April 10, 2026, 5:04 a.m.