Triple
T36618583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metro Manila Skyway |
E903672
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object |
San Juan
San Juan is a highly urbanized city in Metro Manila, Philippines, known for its historical sites, commercial centers, and dense residential communities.
|
E274337
|
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: San Juan | Statement: [Metro Manila Skyway, passesThrough, San Juan]
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: San Juan Triple: [Metro Manila Skyway, passesThrough, San Juan]
Generated description
San Juan is a highly urbanized city in Metro Manila, Philippines, known for its historical sites, commercial centers, and dense residential communities.
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_69f76e6960e4819092047756ceb9a17e |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7c4833d9c8190a6c582fd826c2c3b |
completed | May 3, 2026, 9:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3a095f93688190894e7234cda0ef1d |
completed | June 23, 2026, 4:19 a.m. |
| NEDg | Description generation | batch_6a3a0c260b908190bf21dba3b76933cf |
completed | June 23, 2026, 4:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a3a0cb8da8c8190916b241556ff7846 |
completed | June 23, 2026, 4:34 a.m. |
Created at: May 3, 2026, 4:11 p.m.