Triple
T11979750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tierney |
E285126
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Maura Tierney |
E262184
|
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: Maura Tierney | Statement: [Tierney, hasNotableBearer, Maura Tierney]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maura Tierney Context triple: [Tierney, hasNotableBearer, Maura Tierney]
-
A.
Maura Tierney
chosen
Maura Tierney is an American actress best known for her roles on the television series "ER" and "NewsRadio," as well as in various film and stage productions.
-
B.
Mary-Louise Parker
Mary-Louise Parker is an American actress best known for her roles in the television series "Weeds" and numerous acclaimed film and stage performances.
-
C.
Elizabeth Berkley
Elizabeth Berkley is an American actress best known for her roles in the TV series "Saved by the Bell" and the film "Showgirls."
-
D.
Téa Leoni
Téa Leoni is an American actress and producer best known for her leading roles in film and television, including the political drama series "Madam Secretary."
-
E.
Maura West
Maura West is an American actress best known for her long-running, Emmy-winning work in daytime soap operas.
- 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_69d6ab2eaeb881909f7914758f859413 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90393cfb08190b5b45d3e5e32fad3 |
completed | April 10, 2026, 2:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f47209bd088190bf4c7687c0a5eed6 |
completed | May 1, 2026, 9:27 a.m. |
Created at: April 8, 2026, 9:46 p.m.