Triple
T6395617
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | C-5 |
E143932
|
entity |
| Predicate | hasStation |
P35
|
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: [C-5, hasStation, Alcorcón]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alcorcón Context triple: [C-5, hasStation, 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.
Vallecas
Vallecas is a district in the southeast of Madrid, Spain, known for its working-class roots, strong local identity, and vibrant community life.
-
D.
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.
-
E.
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.
- 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_69c008db906c819096f3597d55d95432 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0688275d0819086b58123c743a6db |
completed | March 22, 2026, 10:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6e40980d08190930db5f9ce577a0e |
completed | March 27, 2026, 8:09 p.m. |
Created at: March 22, 2026, 4:35 p.m.