Triple

T26998441
Position Surface form Disambiguated ID Type / Status
Subject Helen Joseph Hospital E680038 entity
Predicate formerlyKnownAs P65 FINISHED
Object J G Strijdom Hospital
J G Strijdom Hospital was a major public hospital in Johannesburg, South Africa, later renamed Helen Joseph Hospital after the anti-apartheid activist.
E1583130 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: J G Strijdom Hospital | Statement: [Helen Joseph Hospital, formerlyKnownAs, J G Strijdom Hospital]
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: J G Strijdom Hospital
Triple: [Helen Joseph Hospital, formerlyKnownAs, J G Strijdom Hospital]
Generated description
J G Strijdom Hospital was a major public hospital in Johannesburg, South Africa, later renamed Helen Joseph Hospital after the anti-apartheid activist.

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_69eeeb52908c8190bd246244686aa455 completed April 27, 2026, 4:51 a.m.
NER Named-entity recognition batch_69f62196241c8190a52462f0f93b3998 completed May 2, 2026, 4:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1247ef7d588190bac6313a288b2153 completed May 24, 2026, 12:35 a.m.
NEDg Description generation batch_6a1249973fe48190ac773c774941b397 completed May 24, 2026, 12:43 a.m.
NED2 Entity disambiguation (via description) batch_6a124a50c12c8190a37b7286847e7b12 completed May 24, 2026, 12:46 a.m.
Created at: April 27, 2026, 6:56 a.m.