Triple
T1804223
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marcia Lucas |
E39785
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Marcia |
E100090
|
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: Marcia | Statement: [Marcia Lucas, givenName, Marcia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marcia Context triple: [Marcia Lucas, givenName, Marcia]
-
A.
Marcia
chosen
Marcia was the mother of the Roman emperor Trajan and a member of the provincial Roman aristocracy in Hispania.
-
B.
Diane
Diane is a feminine given name of Latin origin, derived from the name of the Roman goddess Diana.
-
C.
Sandra
Sandra is the given name of Sandra Day O’Connor, the first woman to serve as a Justice on the United States Supreme Court.
-
D.
Paula
Paula is a feminine given name used in many languages, derived from the Latin name Paulus meaning "small" or "humble."
-
E.
Jacqueline
Jacqueline is a feminine given name most famously borne by former U.S. First Lady Jacqueline Kennedy Onassis.
- 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_69a88632aa588190ba3978fde0db5bbd |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa659648e8819085fafb60dc03f14b |
completed | March 6, 2026, 5:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae0ab743fc8190b181929109642e36 |
completed | March 8, 2026, 11:48 p.m. |
Created at: March 4, 2026, 7:32 p.m.