Triple
T13855380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angela Chase |
E333049
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Angela |
E10812
|
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: Angela | Statement: [Angela Chase, givenName, Angela]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Angela Context triple: [Angela Chase, givenName, Angela]
-
A.
Angela
Angela is a minor character in the dark comedy crime film "Seven Psychopaths," appearing in the story’s ensemble of eccentric and violent figures.
-
B.
Angela
Angela is the heroine of Matthew Lewis's Gothic melodrama "The Castle Spectre," central to its tale of mystery, romance, and supernatural intrigue.
-
C.
Angela
Angela is a central character in the film "Street Angel," around whom the story’s emotional and dramatic events revolve.
-
D.
Angela
Angela is the Italian surname of the prominent scientific popularizer and television host Piero Angela.
-
E.
Angela
chosen
Angela is the given name of Angela Merkel, the long-serving former Chancellor of Germany and a prominent European political leader.
- 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_69d81c5ba13c8190839315f54768acfd |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02db9c9c81909bb2d2fbfb7394b1 |
completed | April 14, 2026, 9:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c70a59e8819090b750699993a107 |
completed | May 3, 2026, 10:07 p.m. |
Created at: April 9, 2026, 10:14 p.m.