Triple

T8350697
Position Surface form Disambiguated ID Type / Status
Subject Nergal E196148 entity
Predicate hasTemple P1191 FINISHED
Object E-Meslam in Kutha
E-Meslam in Kutha was an ancient Mesopotamian temple complex dedicated to the underworld god Nergal in the city of Kutha.
E727451 NE FINISHED

How this triple was built (4 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: E-Meslam in Kutha | Statement: [Nergal, hasTemple, E-Meslam in Kutha]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: E-Meslam in Kutha
Context triple: [Nergal, hasTemple, E-Meslam in Kutha]
  • A. Mezallat
    Mezallat is a passenger station on Cairo Metro’s Line 2 serving commuters in the Greater Cairo area.
  • B. Shalateen
    Shalateen is a remote Egyptian town near the Sudanese border, known for its Bedouin communities, camel markets, and strategic location along the Red Sea coast.
  • C. Mit Ghamr
    Mit Ghamr is an industrial and commercial city in Egypt’s Nile Delta, known historically as a center for aluminum production and early Islamic banking experiments.
  • D. Multazam
    Multazam is the small sacred area between the Black Stone and the door of the Kaaba where pilgrims supplicate, believing prayers there are especially accepted.
  • E. MV Kaleetan
    MV Kaleetan is a Washington State Ferries vessel that operates as a car and passenger ferry in the Puget Sound region of Washington State.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: E-Meslam in Kutha
Triple: [Nergal, hasTemple, E-Meslam in Kutha]
Generated description
E-Meslam in Kutha was an ancient Mesopotamian temple complex dedicated to the underworld god Nergal in the city of Kutha.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: E-Meslam in Kutha
Target entity description: E-Meslam in Kutha was an ancient Mesopotamian temple complex dedicated to the underworld god Nergal in the city of Kutha.
  • A. Mezallat
    Mezallat is a passenger station on Cairo Metro’s Line 2 serving commuters in the Greater Cairo area.
  • B. Shalateen
    Shalateen is a remote Egyptian town near the Sudanese border, known for its Bedouin communities, camel markets, and strategic location along the Red Sea coast.
  • C. Mit Ghamr
    Mit Ghamr is an industrial and commercial city in Egypt’s Nile Delta, known historically as a center for aluminum production and early Islamic banking experiments.
  • D. Multazam
    Multazam is the small sacred area between the Black Stone and the door of the Kaaba where pilgrims supplicate, believing prayers there are especially accepted.
  • E. MV Kaleetan
    MV Kaleetan is a Washington State Ferries vessel that operates as a car and passenger ferry in the Puget Sound region of Washington State.
  • F. None of above. chosen

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_69ca82edd63c8190b876b8465464c5fa completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb80181ca48190bbf2e6a6aae80d69 completed March 31, 2026, 8:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69cdc74e11f881908b52d0ffea751c96 completed April 2, 2026, 1:33 a.m.
NEDg Description generation batch_69cdcc86626c8190a4206feedea24b41 completed April 2, 2026, 1:55 a.m.
NED2 Entity disambiguation (via description) batch_69cdcde02e088190be8220f7d18d6700 completed April 2, 2026, 2:01 a.m.
Created at: March 30, 2026, 5:59 p.m.