Triple

T26612002
Position Surface form Disambiguated ID Type / Status
Subject Downtown Indianapolis E667948 entity
Predicate hasLandmark P105 FINISHED
Object Veterans Memorial Plaza
Veterans Memorial Plaza is a prominent public space in downtown Indianapolis dedicated to honoring U.S. military veterans through monuments, green space, and commemorative architecture.
E1746098 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: Veterans Memorial Plaza | Statement: [Downtown Indianapolis, hasLandmark, Veterans Memorial Plaza]
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: Veterans Memorial Plaza
Triple: [Downtown Indianapolis, hasLandmark, Veterans Memorial Plaza]
Generated description
Veterans Memorial Plaza is a prominent public space in downtown Indianapolis dedicated to honoring U.S. military veterans through monuments, green space, and commemorative architecture.

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_69ee9cfe16088190a3dddd68e3c7b1ea completed April 26, 2026, 11:17 p.m.
NER Named-entity recognition batch_69f615a93e3c8190a569c4d548da9900 completed May 2, 2026, 3:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a121e7b4b34819097302674c5722801 completed May 23, 2026, 9:39 p.m.
NEDg Description generation batch_6a121f0ade0481909f63ec824a028120 completed May 23, 2026, 9:41 p.m.
NED2 Entity disambiguation (via description) batch_6a121f80ab148190a642473aff894d9c completed May 23, 2026, 9:43 p.m.
Created at: April 27, 2026, 2:17 a.m.