Triple
T26429437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mediapro |
E664460
|
entity |
| Predicate | foundedBy |
P104
|
FINISHED |
| Object |
Gerard Romy
Gerard Romy is a Spanish media executive best known as a co-founder and key leader of the international sports and media group Mediapro.
|
E1724207
|
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: Gerard Romy | Statement: [Mediapro, foundedBy, Gerard Romy]
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: Gerard Romy Triple: [Mediapro, foundedBy, Gerard Romy]
Generated description
Gerard Romy is a Spanish media executive best known as a co-founder and key leader of the international sports and media group Mediapro.
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_69ee883ad6a4819088f918e76122d690 |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69f611bd3ec0819080f559e2cb3889a0 |
completed | May 2, 2026, 3:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a11aec878e0819084561b4a97d8a90c |
completed | May 23, 2026, 1:42 p.m. |
| NEDg | Description generation | batch_6a11af9c1be081909d2e461e3da596d6 |
completed | May 23, 2026, 1:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a11b051d328819090f947755dda4cfc |
completed | May 23, 2026, 1:49 p.m. |
Created at: April 26, 2026, 11:47 p.m.