Triple
T7735130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sydney Brenner |
E175359
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Brenner |
E373850
|
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: Brenner | Statement: [Sydney Brenner, familyName, Brenner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brenner Context triple: [Sydney Brenner, familyName, Brenner]
-
A.
Brenner
chosen
Brenner is a surname of German origin borne by various notable individuals across fields such as science, politics, and the arts.
-
B.
Brenz
The Brenz is a river in southern Germany that flows through Baden-Württemberg and Bavaria before joining the Danube.
-
C.
Breier
Breier is a surname of German origin borne by various individuals across different fields.
-
D.
Borgne
Borgne is a coastal commune in northern Haiti known for its rural communities, fishing activities, and agricultural production.
-
E.
Brinkin
Brinkin is a coastal residential suburb in Darwin, Northern Territory, known for its proximity to Charles Darwin University and Casuarina Beach.
- 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_69c6995e912c81909a49a2657103f786 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7033afbb881909b7ed2cc8f6c27c3 |
completed | March 27, 2026, 10:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8b534b6588190885db4632b97775f |
completed | March 29, 2026, 5:14 a.m. |
Created at: March 27, 2026, 4:06 p.m.