Triple
T14085538
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Torrelaguna |
E338983
|
entity |
| Predicate | roadAccess |
P385
|
FINISHED |
| Object |
M-134
M-134 is a regional road in the Community of Madrid, Spain, that provides access to and from the town of Torrelaguna and connects it with nearby localities.
|
E1080866
|
NE FINISHED |
How this triple was built (4 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: M-134 | Statement: [Torrelaguna, roadAccess, M-134]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: M-134 Context triple: [Torrelaguna, roadAccess, M-134]
-
A.
M-14
M-14 is a state highway in southeastern Michigan that serves as a major commuter route connecting Ann Arbor with the western suburbs of the Detroit metropolitan area.
-
B.
M-13
M-13 is a state highway in Michigan that runs through the Saginaw Bay region, connecting communities such as Essexville with the broader state road network.
-
C.
M-45
M-45 is a state highway in Michigan that runs east–west through the Grand Rapids area toward the Lake Michigan shoreline.
-
D.
M-45
M-45 is a major ring road in the Madrid metropolitan area that helps divert traffic around the city and connect several eastern suburbs.
-
E.
M-119
M-119 is a scenic state highway in northern Michigan renowned for its winding "Tunnel of Trees" route along the Lake Michigan shoreline.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: M-134 Triple: [Torrelaguna, roadAccess, M-134]
Generated description
M-134 is a regional road in the Community of Madrid, Spain, that provides access to and from the town of Torrelaguna and connects it with nearby localities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: M-134 Target entity description: M-134 is a regional road in the Community of Madrid, Spain, that provides access to and from the town of Torrelaguna and connects it with nearby localities.
-
A.
M-14
M-14 is a state highway in southeastern Michigan that serves as a major commuter route connecting Ann Arbor with the western suburbs of the Detroit metropolitan area.
-
B.
M-13
M-13 is a state highway in Michigan that runs through the Saginaw Bay region, connecting communities such as Essexville with the broader state road network.
-
C.
M-45
M-45 is a state highway in Michigan that runs east–west through the Grand Rapids area toward the Lake Michigan shoreline.
-
D.
M-45
M-45 is a major ring road in the Madrid metropolitan area that helps divert traffic around the city and connect several eastern suburbs.
-
E.
M-119
M-119 is a scenic state highway in northern Michigan renowned for its winding "Tunnel of Trees" route along the Lake Michigan shoreline.
- F. None of above. chosen
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_69d81c687b0c819087fd9ed4198403f8 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de5edff1b881909ea56dc2429ef2dd |
completed | April 14, 2026, 3:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcd0a3e55c81909b52f618e9076dd2 |
completed | May 7, 2026, 5:49 p.m. |
| NEDg | Description generation | batch_69fcd2ae45108190b1f400e4ae16a258 |
completed | May 7, 2026, 5:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fcd3ad7be8819094fc71c9f44fb4cb |
completed | May 7, 2026, 6:02 p.m. |
Created at: April 9, 2026, 10:21 p.m.