Triple

T1499792
Position Surface form Disambiguated ID Type / Status
Subject Habana Labs E29769 entity
Predicate hasOfficeLocation P1268 FINISHED
Object Tel Aviv E11499 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: Tel Aviv | Statement: [Habana Labs, hasOfficeLocation, Tel Aviv]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tel Aviv
Context triple: [Habana Labs, hasOfficeLocation, Tel Aviv]
  • A. Tel Aviv chosen
    Tel Aviv is a major Israeli coastal city known for its vibrant nightlife, high-tech industry, and modernist architecture.
  • B. 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.
  • C. Petah Tikva
    Petah Tikva is a major city in central Israel, known as one of the country’s oldest modern Jewish settlements and a significant industrial and commercial hub in the Tel Aviv metropolitan area.
  • D. Herzliya
    Herzliya is a coastal city in central Israel known as a high-tech and academic hub, home to major technology companies and institutions.
  • E. Haifa
    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.
  • 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_69a498dba1d8819093b46a3a8d2485f1 completed March 1, 2026, 7:51 p.m.
NER Named-entity recognition batch_69a4c6f2d7f881909188a3e5614335cd completed March 1, 2026, 11:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad468cd3b88190916a6363884d664b completed March 8, 2026, 9:51 a.m.
Created at: March 1, 2026, 8:12 p.m.