Triple
T32456474
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Outfit |
E829440
|
entity |
| Predicate | characterPortrayedByKarenBlack |
P198420
|
FINISHED |
| Object | Bett Harrow |
—
|
NE NERFINISHED |
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: Bett Harrow | Statement: [The Outfit, characterPortrayedByKarenBlack, Bett Harrow]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterPortrayedByKarenBlack Context triple: [The Outfit, characterPortrayedByKarenBlack, Bett Harrow]
-
A.
characterPortrayedByRuthGordon
Indicates that a given character is portrayed or played by the actress Ruth Gordon.
-
B.
characterPlayedBy Shirley Eaton
Indicates that a specific character is portrayed or acted by Shirley Eaton.
-
C.
characterPlayedByKathleenQuinlan
Indicates that a given character is portrayed or acted by Kathleen Quinlan.
-
D.
characterPortrayedBySandrineBonnaire
Indicates that a character is portrayed or played by the actress Sandrine Bonnaire.
-
E.
characterPlayedByClaudetteColbert
Indicates that a given character is portrayed or acted by Claudette Colbert.
- F. None of above. chosen
Provenance (4 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_69f3491df9288190afc0b23b1d6e72ce |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fee25dbca481909e6f1c255122b3a8 |
completed | May 9, 2026, 7:29 a.m. |
| PD | Predicate disambiguation | batch_69fee1c8915c8190b08b63e42881f1a9 |
completed | May 9, 2026, 7:27 a.m. |
| PDg | Predicate description generation | batch_69fee25c7c548190a2c6e50074a33da4 |
completed | May 9, 2026, 7:29 a.m. |
Created at: May 1, 2026, 12:56 a.m.