Triple
T15996430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Mark Greene |
E387977
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Jennifer Greene |
E1183661
|
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: Jennifer Greene | Statement: [Dr. Mark Greene, spouse, Jennifer Greene]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jennifer Greene Context triple: [Dr. Mark Greene, spouse, Jennifer Greene]
-
A.
Jennifer Greene
chosen
Jennifer Greene is a fictional character from the television series "ER," known primarily as the wife of Dr. Mark Greene.
-
B.
Laura H. Greene
Laura H. Greene is an American physicist renowned for her research in condensed matter physics and for her leadership in the scientific community.
-
C.
Sarah Green
Sarah Green is an American film producer known for her frequent collaborations with director Terrence Malick on critically acclaimed independent films.
-
D.
Sarah Green
Sarah Green is a vocalist known for her guest appearance on Lupe Fiasco’s acclaimed hip-hop album "Food & Liquor."
-
E.
Sarah Green
Sarah Green is a British Liberal Democrat politician who serves as the Member of Parliament for the Chesham and Amersham constituency.
- 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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e157882ef0819081143e530bd6413c |
completed | April 16, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3d79dec8190b02e003f93e5dad6 |
completed | May 9, 2026, 11:31 p.m. |
Created at: April 10, 2026, 4:55 a.m.