Triple

T29359643

Position	Surface form	Disambiguated ID	Type / Status
Subject	San José de Flores	`E744550`	entity
Predicate	hasConnection	`P8776`	FINISHED
Object	Flores railway station Flores railway station is a commuter rail station in the Flores neighborhood of Buenos Aires, Argentina, serving as a key stop on the city’s suburban rail network.	`E1864427`	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: Flores railway station | Statement: [San José de Flores, hasConnection, Flores railway 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: Flores railway station
Triple: [San José de Flores, hasConnection, Flores railway station]

Generated description

Flores railway station is a commuter rail station in the Flores neighborhood of Buenos Aires, Argentina, serving as a key stop on the city’s suburban rail network.

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_69f0a79aee588190b490f19d93c6e52d`	completed	April 28, 2026, 12:27 p.m.
NER	Named-entity recognition	`batch_69f669876fa08190959631b8d828978b`	completed	May 2, 2026, 9:15 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a25c0fa08cc8190b17dcac635e24556`	completed	June 7, 2026, 7:05 p.m.
NEDg	Description generation	`batch_6a25c6138fac819094ef0f14303f75f9`	completed	June 7, 2026, 7:27 p.m.
NED2	Entity disambiguation (via description)	`batch_6a25c9ef476481908009305d31b9ca58`	completed	June 7, 2026, 7:43 p.m.

Created at: April 28, 2026, 2:16 p.m.