Triple
T2759750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 5 (Santiago Metro) |
E61190
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Laguna Sur station
Laguna Sur station is a stop on Santiago, Chile’s Metro system serving Line 5 in the city’s western sector.
|
E308518
|
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: Laguna Sur station | Statement: [Line 5 (Santiago Metro), hasStation, Laguna Sur station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laguna Sur station Context triple: [Line 5 (Santiago Metro), hasStation, Laguna Sur station]
-
A.
Santiago Bueras station
Santiago Bueras station is an underground stop on Santiago’s Metro network serving Line 5 in the western part of the city.
-
B.
Ortigas station
Ortigas station is an elevated rapid transit stop serving the Ortigas Center business district in Metro Manila, Philippines.
-
C.
San Carlos station
San Carlos station is a commuter rail station in San Carlos, California, serving Caltrain passengers on the San Francisco Peninsula.
-
D.
Antipolo station
Antipolo station is an elevated eastern terminal station of Manila’s LRT Line 2 serving the city of Antipolo in Rizal, Philippines.
-
E.
Buendia station
Buendia station is an elevated rapid transit stop on Manila's MRT Line 3 serving the busy Buendia Avenue area in Makati, Philippines.
- 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: Laguna Sur station Triple: [Line 5 (Santiago Metro), hasStation, Laguna Sur station]
Generated description
Laguna Sur station is a stop on Santiago, Chile’s Metro system serving Line 5 in the city’s western sector.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Laguna Sur station Target entity description: Laguna Sur station is a stop on Santiago, Chile’s Metro system serving Line 5 in the city’s western sector.
-
A.
Santiago Bueras station
Santiago Bueras station is an underground stop on Santiago’s Metro network serving Line 5 in the western part of the city.
-
B.
Ortigas station
Ortigas station is an elevated rapid transit stop serving the Ortigas Center business district in Metro Manila, Philippines.
-
C.
San Carlos station
San Carlos station is a commuter rail station in San Carlos, California, serving Caltrain passengers on the San Francisco Peninsula.
-
D.
Antipolo station
Antipolo station is an elevated eastern terminal station of Manila’s LRT Line 2 serving the city of Antipolo in Rizal, Philippines.
-
E.
Buendia station
Buendia station is an elevated rapid transit stop on Manila's MRT Line 3 serving the busy Buendia Avenue area in Makati, Philippines.
- 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_69ab4b7bab6c8190a5c2efef19a8ef34 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdd2121548190b96f174e6f61f9b5 |
completed | March 7, 2026, 8:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b03121c93481909bee6711fab0ee0a |
completed | March 10, 2026, 2:56 p.m. |
| NEDg | Description generation | batch_69b03bf377e481908542a133a2d06510 |
completed | March 10, 2026, 3:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b044396e3881908c5b11f7d9612189 |
completed | March 10, 2026, 4:18 p.m. |
Created at: March 6, 2026, 9:57 p.m.