Triple
T16759261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wind Fell |
E407296
|
entity |
| Predicate | category |
P87
|
FINISHED |
| Object | Marilyn |
E35887
|
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: Marilyn | Statement: [Wind Fell, category, Marilyn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marilyn Context triple: [Wind Fell, category, Marilyn]
-
A.
Marilyn
chosen
A Marilyn is a type of British hill or mountain classified by having a prominence of at least 150 meters, regardless of its absolute height.
-
B.
Marilyn
Marilyn is the given first name of American country music singer Jeannie Seely.
-
C.
Marilyn
Marilyn is the middle name of Toni Marilyn Smith.
-
D.
Marlene
Marlene is an energetic and friendly otter who appears as a main supporting character in the animated series "The Penguins of Madagascar."
-
E.
Marlene
Marlene is the ambitious, career-driven protagonist of Caryl Churchill’s play "Top Girls," whose life embodies the tensions between feminism, success, and personal sacrifice.
- 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_69d8839174188190909f190097207065 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3abeb3ab08190918f6bff686858be |
completed | April 18, 2026, 4:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00a52b17b88190af24c04d16980c9f |
completed | May 10, 2026, 3:32 p.m. |
Created at: April 10, 2026, 5:21 a.m.