Triple
T11680097
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LRT Line 1 |
E277591
|
entity |
| Predicate | depot |
P14646
|
FINISHED |
| Object |
Baclaran Depot
Baclaran Depot is the main maintenance and stabling facility serving Manila’s LRT Line 1 near its southern terminus in Baclaran.
|
E940558
|
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: Baclaran Depot | Statement: [LRT Line 1, depot, Baclaran Depot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baclaran Depot Context triple: [LRT Line 1, depot, Baclaran Depot]
-
A.
Peñablanca station
Peñablanca station is a passenger rail stop on Chile’s Valparaíso Metro system serving the Peñablanca area in the Valparaíso Region.
-
B.
Cagayan de Oro Station
Cagayan de Oro Station is a regional station of the Philippine Court of Appeals serving the judicial needs of Northern Mindanao and nearby areas.
-
C.
Antipolo station
Antipolo station is an elevated eastern terminal station of Manila’s LRT Line 2 serving the city of Antipolo in Rizal, Philippines.
-
D.
Munyang Depot
Munyang Depot is a maintenance and storage facility serving the Daegu Metro system in Daegu, South Korea.
-
E.
Katipunan station
Katipunan station is an elevated rapid transit stop in Quezon City, Philippines, serving the Katipunan Avenue area and nearby universities on Manila’s LRT Line 2.
- 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: Baclaran Depot Triple: [LRT Line 1, depot, Baclaran Depot]
Generated description
Baclaran Depot is the main maintenance and stabling facility serving Manila’s LRT Line 1 near its southern terminus in Baclaran.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Baclaran Depot Target entity description: Baclaran Depot is the main maintenance and stabling facility serving Manila’s LRT Line 1 near its southern terminus in Baclaran.
-
A.
Peñablanca station
Peñablanca station is a passenger rail stop on Chile’s Valparaíso Metro system serving the Peñablanca area in the Valparaíso Region.
-
B.
Cagayan de Oro Station
Cagayan de Oro Station is a regional station of the Philippine Court of Appeals serving the judicial needs of Northern Mindanao and nearby areas.
-
C.
Antipolo station
Antipolo station is an elevated eastern terminal station of Manila’s LRT Line 2 serving the city of Antipolo in Rizal, Philippines.
-
D.
Munyang Depot
Munyang Depot is a maintenance and storage facility serving the Daegu Metro system in Daegu, South Korea.
-
E.
Katipunan station
Katipunan station is an elevated rapid transit stop in Quezon City, Philippines, serving the Katipunan Avenue area and nearby universities on Manila’s LRT Line 2.
- 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_69d6aafd0a448190b44da30af8c6c519 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a461b0908190bef4e1c6777affcf |
completed | April 10, 2026, 7:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ef14007dd08190b60640be9949ca26 |
completed | April 27, 2026, 7:45 a.m. |
| NEDg | Description generation | batch_69ef35527f908190b681afdae3aec319 |
completed | April 27, 2026, 10:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ef51ec07ec8190b5cd97cf909388f0 |
completed | April 27, 2026, 12:09 p.m. |
Created at: April 8, 2026, 9:40 p.m.