Triple
T5133018
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ronas Hill |
E115746
|
entity |
| Predicate | hasListing |
P1278
|
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: [Ronas Hill, hasListing, Marilyn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marilyn Context triple: [Ronas Hill, hasListing, 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.
Marlene
Marlene is a German biographical film directed by Joseph Vilsmaier about the life and career of actress and singer Marlene Dietrich.
-
C.
Marilyn Monroe
Marilyn Monroe was an iconic American actress, model, and sex symbol of the mid-20th century, renowned for her comedic roles, glamorous image, and enduring cultural legacy.
-
D.
Gloria
Gloria is a joyful hymn of praise in Christian liturgy, traditionally sung during major celebrations such as the Easter Vigil.
-
E.
Gloria
Gloria is a central human character in the 2023 film "Barbie," portrayed as a Mattel employee and mother whose personal struggles and imagination help bridge the real world with Barbie Land.
- 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_69bd444426bc819099ccd23f141e22aa |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd784b477c8190926daddb28a255af |
completed | March 20, 2026, 4:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bec4c9a14881908a8bf2f73ebf56f7 |
completed | March 21, 2026, 4:18 p.m. |
Created at: March 20, 2026, 1:42 p.m.