Triple
T8058932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brendon |
E188066
|
entity |
| Predicate | hasStressPatternInEnglish |
P4253
|
FINISHED |
| Object |
BREN-don
BREN-don is the stressed syllable pattern of the English given name "Brendon," indicating primary stress on the first syllable.
|
E707201
|
NE FINISHED |
How this triple was built (4 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: BREN-don | Statement: [Brendon, hasStressPatternInEnglish, BREN-don]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BREN-don Context triple: [Brendon, hasStressPatternInEnglish, BREN-don]
-
A.
Brenz
The Brenz is a river in southern Germany that flows through Baden-Württemberg and Bavaria before joining the Danube.
-
B.
Brenner
Brenner is a surname of German origin borne by various notable individuals across fields such as science, politics, and the arts.
-
C.
Breng
Breng is a Dutch public transport operator providing regional bus and train services in and around Arnhem and Nijmegen in the Netherlands.
-
D.
Brinkin
Brinkin is a coastal residential suburb in Darwin, Northern Territory, known for its proximity to Charles Darwin University and Casuarina Beach.
-
E.
Bron/Broen
Bron/Broen is a Scandinavian crime drama television series that follows a joint Danish-Swedish police investigation into murders that occur on the Øresund Bridge connecting the two countries.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: BREN-don Triple: [Brendon, hasStressPatternInEnglish, BREN-don]
Generated description
BREN-don is the stressed syllable pattern of the English given name "Brendon," indicating primary stress on the first syllable.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: BREN-don Target entity description: BREN-don is the stressed syllable pattern of the English given name "Brendon," indicating primary stress on the first syllable.
-
A.
Brenz
The Brenz is a river in southern Germany that flows through Baden-Württemberg and Bavaria before joining the Danube.
-
B.
Brenner
Brenner is a surname of German origin borne by various notable individuals across fields such as science, politics, and the arts.
-
C.
Breng
Breng is a Dutch public transport operator providing regional bus and train services in and around Arnhem and Nijmegen in the Netherlands.
-
D.
Brinkin
Brinkin is a coastal residential suburb in Darwin, Northern Territory, known for its proximity to Charles Darwin University and Casuarina Beach.
-
E.
Bron/Broen
Bron/Broen is a Scandinavian crime drama television series that follows a joint Danish-Swedish police investigation into murders that occur on the Øresund Bridge connecting the two countries.
- F. None of above. chosen
Provenance (5 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_69ca82b2f68881908c50560697e210da |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3fa51adc8190b9327441bd8b9fb3 |
completed | March 31, 2026, 3:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc572a87788190a92f96f7b9c43f2a |
completed | March 31, 2026, 11:22 p.m. |
| NEDg | Description generation | batch_69cc58ee56108190b93f0bf6bbb0321c |
completed | March 31, 2026, 11:29 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cc5cd36a108190a4aeff02358875f1 |
completed | March 31, 2026, 11:46 p.m. |
Created at: March 30, 2026, 5:25 p.m.