Triple
T13036427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Phyllis Lindstrom |
E326571
|
entity |
| Predicate | hasRelative |
P367
|
FINISHED |
| Object | Bess Lindstrom |
E1050145
|
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: Bess Lindstrom | Statement: [Phyllis Lindstrom, hasRelative, Bess Lindstrom]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bess Lindstrom Context triple: [Phyllis Lindstrom, hasRelative, Bess Lindstrom]
-
A.
Bess Lindstrom
chosen
Bess Lindstrom is a fictional character from the television series "The Mary Tyler Moore Show" and its spin-off "Phyllis," known as the daughter of Phyllis Lindstrom.
-
B.
Betsy Rue
Betsy Rue is an American actress best known for her roles in horror and thriller films, including her appearance in the slasher movie "My Bloody Valentine 3D."
-
C.
Tessa Berens
Tessa Berens is a fictional character from the work titled "The Silence."
-
D.
Bridget Strand
Bridget Strand is a central character in the video game "Death Stranding," serving as the visionary U.S. president whose actions and legacy drive much of the game's narrative.
-
E.
Laura Bickford
Laura Bickford is an American film producer best known for her work on acclaimed independent and studio films, including the Oscar-winning drama "Traffic."
- 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97f2a71a0819098bb6cf8a4b2208a |
completed | April 10, 2026, 10:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7ce5b2b988190892e14620fb87366 |
completed | May 3, 2026, 10:38 p.m. |
Created at: April 9, 2026, 8:55 p.m.