Triple

T293126
Position Surface form Disambiguated ID Type / Status
Subject Israel E6036 entity
Predicate majorCity P316 FINISHED
Object Haifa E12305 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: Haifa | Statement: [Israel, majorCity, Haifa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Haifa
Context triple: [Israel, majorCity, Haifa]
  • A. Haifa chosen
    Haifa is a major Israeli city on the Mediterranean coast, known for its significant port, mixed Jewish-Arab population, and the terraced Baháʼí Gardens on Mount Carmel.
  • B. Tel Aviv
    Tel Aviv is a major Israeli coastal city known for its vibrant nightlife, high-tech industry, and modernist architecture.
  • C. Tiberias
    Tiberias is an ancient city in northern Israel on the western shore of the Sea of Galilee, historically significant as a major center of Jewish learning and pilgrimage.
  • D. Gaza City
    Gaza City is a densely populated urban center on the Mediterranean coast that serves as the political, economic, and cultural hub of the Gaza Strip.
  • E. Ramat Gan
    Ramat Gan is a city in the Tel Aviv District of Israel, known for its diamond exchange district, business centers, and large urban park.
  • 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_69a2e79114b081909490b3bf5a5dbb51 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2e976f32081908042485c4530e1e2 completed Feb. 28, 2026, 1:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3e011b4f08190a859bde5bdd40f21 completed March 1, 2026, 6:43 a.m.
Created at: Feb. 28, 2026, 1:06 p.m.