Triple

T26305773
Position Surface form Disambiguated ID Type / Status
Subject University of Zulia E661677 entity
Predicate hasFaculty P141 FINISHED
Object Faculty of Law
The Faculty of Law at the University of Zulia is an academic unit dedicated to legal education, research, and professional training in law within this Venezuelan public university.
 E1720925 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: Faculty of Law | Statement: [University of Zulia, hasFaculty, Faculty of Law]
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: Faculty of Law
Triple: [University of Zulia, hasFaculty, Faculty of Law]
Generated description
The Faculty of Law at the University of Zulia is an academic unit dedicated to legal education, research, and professional training in law within this Venezuelan public university.

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_69ee812dacfc81908484aade9120fba9 completed April 26, 2026, 9:18 p.m.
NER Named-entity recognition batch_69f60ee2f18481908de53cc9872e96f4 completed May 2, 2026, 2:49 p.m.
NED1 Entity disambiguation (via context triple) batch_6a119a53b9688190882851ad936d897d completed May 23, 2026, 12:15 p.m.
NEDg Description generation batch_6a119b1444008190a4cdcbe5fd8bca98 completed May 23, 2026, 12:18 p.m.
NED2 Entity disambiguation (via description) batch_6a119c370ec481909db25ac02d20efd2 completed May 23, 2026, 12:23 p.m.
Created at: April 26, 2026, 10:18 p.m.