Triple
T3042954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rey Juan Carlos University |
E83174
|
entity |
| Predicate | hasCampusIn |
P4623
|
FINISHED |
| Object | Alcorcón |
E95939
|
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: Alcorcón | Statement: [Rey Juan Carlos University, hasCampusIn, Alcorcón]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alcorcón Context triple: [Rey Juan Carlos University, hasCampusIn, Alcorcón]
-
A.
Alcorcón
chosen
Alcorcón is a suburban city in central Spain that forms part of the metropolitan area of Madrid.
-
B.
Móstoles
Móstoles is a major suburban city in central Spain, known as one of the most populous municipalities in the Madrid metropolitan area.
-
C.
Leganés
Leganés is a major suburban city in central Spain, located just southwest of Madrid and known for its residential character, industry, and football club CD Leganés.
-
D.
Fuenlabrada
Fuenlabrada is a large suburban city in central Spain, located southwest of Madrid and known for its rapid growth, industrial activity, and sizable commuter population.
-
E.
Getafe
Getafe is a city in central Spain that forms part of the Madrid metropolitan area and is known for its industrial base, university campus, and air force history.
- 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_69ad8b2298908190a7cb4e9bdbf064d0 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9b5d2a308190b4ce20efcae9b761 |
completed | March 8, 2026, 3:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3bb6216ac8190950d789de6e36aa6 |
completed | March 13, 2026, 7:23 a.m. |
Created at: March 8, 2026, 3:01 p.m.