Triple
T33509548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Father Francis John Patrick Mulcahy |
E858201
|
entity |
| Predicate | laterFictionalRank |
P121402
|
FINISHED |
| Object | Major |
—
|
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: Major | Statement: [Father Francis John Patrick Mulcahy, laterFictionalRank, Major]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: laterFictionalRank Context triple: [Father Francis John Patrick Mulcahy, laterFictionalRank, Major]
-
A.
hasRankInFiction
chosen
Indicates that a fictional character or entity holds a specific rank, title, or hierarchical position within a fictional context or universe.
-
B.
laterSettingOfFiction
Indicates that one fictional work is set chronologically later than another within a shared narrative or story world.
-
C.
rankInBackstory
Indicates the position or status an entity holds within another entity’s narrative background or origin story.
-
D.
laterWrittenIn
Indicates that one text or version was written at a later time than another, establishing a chronological order between the writings.
-
E.
eraOfPopularityInFiction
Indicates the historical time period during which a subject is most commonly or prominently depicted in fictional works.
- F. None of above.
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_69f3497721848190978fbee5e0a526f8 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ffecdcbac4819093b725a7dbe0e61b |
completed | May 10, 2026, 2:26 a.m. |
| PD | Predicate disambiguation | batch_69ffec3633288190adbbd84e277708dc |
completed | May 10, 2026, 2:23 a.m. |
Created at: May 1, 2026, 1:38 a.m.