Triple

T26050045
Position Surface form Disambiguated ID Type / Status
Subject Kiryat Moshe E647945 entity
Predicate hasPublicTransportStop P15438 FINISHED
Object Kiryat Moshe light rail station
Kiryat Moshe light rail station is a stop on the Jerusalem Light Rail system serving the Kiryat Moshe neighborhood in Jerusalem, Israel.
E1708808 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: Kiryat Moshe light rail station | Statement: [Kiryat Moshe, hasPublicTransportStop, Kiryat Moshe light rail station]
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: Kiryat Moshe light rail station
Triple: [Kiryat Moshe, hasPublicTransportStop, Kiryat Moshe light rail station]
Generated description
Kiryat Moshe light rail station is a stop on the Jerusalem Light Rail system serving the Kiryat Moshe neighborhood in Jerusalem, Israel.

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_69e77e8d419481908004e6318d28aaab completed April 21, 2026, 1:41 p.m.
NER Named-entity recognition batch_69f6065d6544819085e13a206bf36916 completed May 2, 2026, 2:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a111b1f46dc81909ce05e7414d8510c completed May 23, 2026, 3:12 a.m.
NEDg Description generation batch_6a111db5c5f88190b8bc2ebf53c9bafb completed May 23, 2026, 3:23 a.m.
NED2 Entity disambiguation (via description) batch_6a111e409b288190891d6cde7af82c7a completed May 23, 2026, 3:25 a.m.
Created at: April 22, 2026, 9:10 a.m.