Triple

T8964424
Position Surface form Disambiguated ID Type / Status
Subject Tadlac E214090 entity
Predicate hasNameInLanguage P15 FINISHED
Object Tadlac@tl E214090 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: Tadlac@tl | Statement: [Tadlac, hasNameInLanguage, Tadlac@tl]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tadlac@tl
Context triple: [Tadlac, hasNameInLanguage, Tadlac@tl]
  • A. Tadlac chosen
    Tadlac is a barangay in the municipality of Los Baños in Laguna province, Philippines, known for its proximity to Tadlac Lake (also called Alligator Lake).
  • B. Tulak
    Tulak is a town in Afghanistan that serves as a local settlement within Ghor Province.
  • C. TLA
    TLA is a formal specification language developed by Leslie Lamport for describing and reasoning about concurrent and distributed systems using temporal logic.
  • D. Tulunan
    Tulunan is a rural municipality in the province of North Cotabato on the island of Mindanao in the Philippines, known primarily for its agricultural economy.
  • E. TAD
    TAD is the OECD’s Trade and Agriculture Directorate, which develops international policies and analysis on global trade, agriculture, and related economic issues.
  • 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_69ca839cd6008190a1546a701a56710c completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc674c4be8819090d46aba8ab40af3 completed April 1, 2026, 12:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfc95514408190ad442069daec0459 completed April 3, 2026, 2:06 p.m.
Created at: March 30, 2026, 7:01 p.m.