Triple

T29749679
Position Surface form Disambiguated ID Type / Status
Subject FR F1 sniper rifle E752858 entity
Predicate nameMeaning P453 FINISHED
Object Fusil à Répétition modèle F1
The Fusil à Répétition modèle F1 is a French bolt-action sniper rifle developed for military precision shooting.
 E1882788 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: Fusil à Répétition modèle F1 | Statement: [FR F1 sniper rifle, nameMeaning, Fusil à Répétition modèle F1]
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: Fusil à Répétition modèle F1
Triple: [FR F1 sniper rifle, nameMeaning, Fusil à Répétition modèle F1]
Generated description
The Fusil à Répétition modèle F1 is a French bolt-action sniper rifle developed for military precision shooting.

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_69f0d62c84cc8190846f80ae04fdf8ec completed April 28, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f6736a13f08190b9695bb8796e0aec completed May 2, 2026, 9:58 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26aa9511488190b76043cd8b962c12 completed June 8, 2026, 11:42 a.m.
NEDg Description generation batch_6a26b5b9f88081908397b3dcaa4f3600 completed June 8, 2026, 12:29 p.m.
NED2 Entity disambiguation (via description) batch_6a26b98d6b14819088d658a7a9678417 completed June 8, 2026, 12:46 p.m.
Created at: April 28, 2026, 7:53 p.m.