Triple

T25773266
Position Surface form Disambiguated ID Type / Status
Subject Philippine expressway network E649076 entity
Predicate hasComponent P35 FINISHED
Object SLEX Toll Road 3
SLEX Toll Road 3 is a major expressway segment in the Philippines that extends the South Luzon Expressway to improve connectivity between Metro Manila and southern Luzon provinces.
E1708886 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: SLEX Toll Road 3 | Statement: [Philippine expressway network, hasComponent, SLEX Toll Road 3]
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: SLEX Toll Road 3
Triple: [Philippine expressway network, hasComponent, SLEX Toll Road 3]
Generated description
SLEX Toll Road 3 is a major expressway segment in the Philippines that extends the South Luzon Expressway to improve connectivity between Metro Manila and southern Luzon provinces.

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_69e7ab333b508190b6d708d8d9a328ed completed April 21, 2026, 4:52 p.m.
NER Named-entity recognition batch_69f5fe5b23ac81908ff1b6f06911f45b completed May 2, 2026, 1:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a111aefdd888190aad47b1eba690f5e completed May 23, 2026, 3:11 a.m.
NEDg Description generation batch_6a111c26bd588190bcb5b6acd9978f06 completed May 23, 2026, 3:16 a.m.
NED2 Entity disambiguation (via description) batch_6a111cfb960c8190ab95d50846acfb91 completed May 23, 2026, 3:20 a.m.
Created at: April 22, 2026, 5:31 a.m.