Triple

T23712458
Position Surface form Disambiguated ID Type / Status
Subject Talaat Harb Street E585902 entity
Predicate hasCentralSquare P15345 FINISHED
Object Talaat Harb Square
Talaat Harb Square is a prominent historic plaza in downtown Cairo, Egypt, known for its central statue of economist Talaat Harb and its surrounding early 20th-century architecture.
E1601335 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: Talaat Harb Square | Statement: [Talaat Harb Street, hasCentralSquare, Talaat Harb Square]
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: Talaat Harb Square
Triple: [Talaat Harb Street, hasCentralSquare, Talaat Harb Square]
Generated description
Talaat Harb Square is a prominent historic plaza in downtown Cairo, Egypt, known for its central statue of economist Talaat Harb and its surrounding early 20th-century architecture.

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_69e24905f77881908194d645676acd60 completed April 17, 2026, 2:51 p.m.
NER Named-entity recognition batch_69f1b7784cd08190a442dd41d56b92b1 completed April 29, 2026, 7:47 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f53b946788190a68215e7219f5f62 completed May 21, 2026, 6:49 p.m.
NEDg Description generation batch_6a0f551a7f648190ac2364cbd1ef3091 completed May 21, 2026, 6:55 p.m.
NED2 Entity disambiguation (via description) batch_6a0f55c4f3fc8190957279b36bbb0ffd completed May 21, 2026, 6:58 p.m.
Created at: April 17, 2026, 6:54 p.m.