Triple
T28124848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laurence Golborne |
E710897
|
entity |
| Predicate | positionHeld |
P8
|
FINISHED |
| Object |
Vice President of Cencosud
The Vice President of Cencosud is a senior executive responsible for helping lead one of Latin America’s largest retail conglomerates, overseeing strategic and operational decisions across its diverse business units.
|
E1801668
|
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: Vice President of Cencosud | Statement: [Laurence Golborne, positionHeld, Vice President of Cencosud]
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: Vice President of Cencosud Triple: [Laurence Golborne, positionHeld, Vice President of Cencosud]
Generated description
The Vice President of Cencosud is a senior executive responsible for helping lead one of Latin America’s largest retail conglomerates, overseeing strategic and operational decisions across its diverse business units.
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_69ef9b73bd288190a21ae3d6aa14f386 |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69f640fb767c819088c4253c3ac6739f |
completed | May 2, 2026, 6:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a15c93806e08190b9902088f506fc61 |
completed | May 26, 2026, 4:24 p.m. |
| NEDg | Description generation | batch_6a15c9d233048190a27d170dbe16c07e |
completed | May 26, 2026, 4:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a15ca83e4588190baed86972447f0f8 |
completed | May 26, 2026, 4:29 p.m. |
Created at: April 27, 2026, 9:19 p.m.