Triple
T36613679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manhattan University |
E903541
|
entity |
| Predicate | hasNotableFacultyInFiction |
P204961
|
FINISHED |
| Object | Jessica Fletcher |
E259425
|
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: Jessica Fletcher | Statement: [Manhattan University, hasNotableFacultyInFiction, Jessica Fletcher]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableFacultyInFiction Context triple: [Manhattan University, hasNotableFacultyInFiction, Jessica Fletcher]
-
A.
hasNotableTeacherFictional
Indicates that a fictional entity has a notable teacher within a narrative or fictional context.
-
B.
hasFictionalAuthor
Indicates that one entity is the fictional or in-universe author of a work attributed to them.
-
C.
hasFictionalAuthorOccupation
Indicates that a fictional author character holds or is associated with a particular occupation or job.
-
D.
hasNotableResidentInFiction
Indicates that a place or entity is notably associated with a fictional character who is depicted as residing there.
-
E.
hasNotableWriter
Indicates that an entity is associated with a writer who is recognized as significant or distinguished in some notable way.
- F. None of above. chosen
Provenance (5 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_69f76e6960e4819092047756ceb9a17e |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037cad051c8190b28b354b89208574 |
completed | May 12, 2026, 7:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3efd9eb478819085c7af1b1620ac83 |
completed | June 26, 2026, 10:30 p.m. |
| PD | Predicate disambiguation | batch_6a037a0bf4b88190bdcfae9a14b51f0a |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82f8c88190bd77a086023ac0e1 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:11 p.m.