Triple
T5753874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Talib Kweli |
E126918
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Greene |
E43976
|
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: Greene | Statement: [Talib Kweli, familyName, Greene]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Greene Context triple: [Talib Kweli, familyName, Greene]
-
A.
Greene
chosen
Greene is a common English surname borne by numerous notable figures in politics, the military, the arts, and other fields.
-
B.
Eldridge
Eldridge is an English-language surname of Old English origin, borne by various notable individuals across fields such as politics, the arts, and sports.
-
C.
Greer
Greer is a surname most notably associated with Hal Greer, a Hall of Fame American basketball player.
-
D.
Greenleaf
Greenleaf is a dramatic television series that explores the secrets, scandals, and power struggles within a wealthy African-American megachurch family.
-
E.
Greenleaf
"Greenleaf" is a short story by Flannery O’Connor that explores themes of faith, violence, and grace through the tense relationship between a farm owner and her hired family.
- 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_69c00832aedc81909899801b141fa3b4 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c029032ba08190ae4062d74ab271ee |
completed | March 22, 2026, 5:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c07e3e71988190a938a6d175023028 |
completed | March 22, 2026, 11:41 p.m. |
Created at: March 22, 2026, 3:48 p.m.