Triple
T9902021
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lope de Vega |
E182307
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Madrid |
E4617
|
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: Madrid | Statement: [Lope de Vega, placeOfBirth, Madrid]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Madrid Context triple: [Lope de Vega, placeOfBirth, Madrid]
-
A.
Madrid
chosen
Madrid is the capital and largest city of Spain, renowned for its rich cultural heritage, historic architecture, and vibrant arts and nightlife scenes.
-
B.
Madrid
Madrid is a municipality in the Cundinamarca department of Colombia, located near Bogotá and known for its floriculture and agricultural production.
-
C.
Madri
Madri is a princess from the Mahabharata epic, known as the second wife of King Pandu and the mother of the twins Nakula and Sahadeva.
-
D.
Seville
Seville is a historic Spanish city in Andalusia renowned for its rich Moorish and Christian heritage, iconic landmarks like the Giralda and Alcázar, and vibrant cultural traditions such as flamenco.
-
E.
Seville
Seville is a small unincorporated rural community located in Volusia County, Florida, known for its agricultural surroundings and historic character.
- 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_69ca82876f8081909cf75df0f99bb13f |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cdb4e221448190b536742d1269f9d7 |
completed | April 2, 2026, 12:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d20d9e132c8190a68f94e4cfd36c56 |
completed | April 5, 2026, 7:22 a.m. |
Created at: March 30, 2026, 8:40 p.m.