Triple
T16898635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madrid public transport network |
E424375
|
entity |
| Predicate | hasZone |
P6793
|
FINISHED |
| Object |
Zone B2
Zone B2 is a fare zone within the Madrid public transport network that covers outer suburban areas beyond the central metropolitan zones.
|
E1239256
|
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: Zone B2 | Statement: [Madrid public transport network, hasZone, Zone B2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zone B2 Context triple: [Madrid public transport network, hasZone, Zone B2]
-
A.
B2 West
B2 West is a regional subdivision within the B2 League, grouping together teams that compete against each other in that specific geographic section of the competition.
-
B.
B2
B2 is the second-generation Volkswagen Passat, produced in the early 1980s and known for its more angular design and expanded body style options compared to its predecessor.
-
C.
B2
B2 is the abbreviated name commonly used to refer to the B2 League sports competition.
-
D.
Zone 2
Zone 2 is a fare zone within a public transit system used to determine ticket prices and travel boundaries.
-
E.
Zone 3
Zone 3 is one of the MBTA Commuter Rail’s outer fare zones used to set ticket prices for trips between Boston and its surrounding suburbs.
- 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: Zone B2 Triple: [Madrid public transport network, hasZone, Zone B2]
Generated description
Zone B2 is a fare zone within the Madrid public transport network that covers outer suburban areas beyond the central metropolitan zones.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Zone B2 Target entity description: Zone B2 is a fare zone within the Madrid public transport network that covers outer suburban areas beyond the central metropolitan zones.
-
A.
B2 West
B2 West is a regional subdivision within the B2 League, grouping together teams that compete against each other in that specific geographic section of the competition.
-
B.
B2
B2 is the abbreviated name commonly used to refer to the B2 League sports competition.
-
C.
B2
B2 is the second-generation Volkswagen Passat, produced in the early 1980s and known for its more angular design and expanded body style options compared to its predecessor.
-
D.
Zone 2
Zone 2 is a fare zone within a public transit system used to determine ticket prices and travel boundaries.
-
E.
Zone 3
Zone 3 is one of the MBTA Commuter Rail’s outer fare zones used to set ticket prices for trips between Boston and its surrounding suburbs.
- 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_69d889da3e8c8190a2b118f383f0beac |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3c8da7b0481909111358871875023 |
completed | April 18, 2026, 6:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00c7b0783c81909c87de503d5e7e3c |
completed | May 10, 2026, 6 p.m. |
| NEDg | Description generation | batch_6a00c830f7ac8190ae25232f88e9774b |
completed | May 10, 2026, 6:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00c8aa5aac8190be5f79f992c8a0ec |
completed | May 10, 2026, 6:04 p.m. |
Created at: April 10, 2026, 5:29 a.m.