Triple

T10337319
Position Surface form Disambiguated ID Type / Status
Subject Mamluk Arabic E243040 entity
Predicate usedIn P98 FINISHED
Object Jerusalem E6995 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: Jerusalem | Statement: [Mamluk Arabic, usedIn, Jerusalem]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jerusalem
Context triple: [Mamluk Arabic, usedIn, Jerusalem]
  • A. Jerusalem chosen
    Jerusalem is an ancient and historically significant city in the Middle East that serves as a major religious and cultural center for Judaism, Christianity, and Islam.
  • B. Jerusalem
    Jerusalem is a novel by Swedish author Selma Lagerlöf that portrays the lives, faith, and emigration of a group of Swedish villagers who journey to the Holy Land.
  • C. Jesusalém
    Jesusalém is a novel by Mozambican writer Mia Couto that explores memory, war, and identity through a boy’s life in an isolated, post-conflict African landscape.
  • D. Jerusalem city center
    Jerusalem city center is the main commercial and cultural hub of Jerusalem, featuring busy shopping streets, historic sites, and key public institutions.
  • E. West Jerusalem
    West Jerusalem is the predominantly Jewish, modern western sector of Jerusalem that has served as the seat of Israel’s government institutions since 1949.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d381af787481908bc401325c760a88 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e0a3a8e4819097268ce101dec2d1 completed April 7, 2026, 10:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69d7506278f881908b090b13706e5d4e completed April 9, 2026, 7:08 a.m.
Created at: April 6, 2026, 11:54 a.m.