Triple
T6693646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dallas urban area |
E152689
|
entity |
| Predicate | hasSuburb |
P747
|
FINISHED |
| Object | Sherman |
E165604
|
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: Sherman | Statement: [Dallas urban area, hasSuburb, Sherman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sherman Context triple: [Dallas urban area, hasSuburb, Sherman]
-
A.
Sherman
chosen
Sherman is a city in north-central Texas that serves as a regional hub for commerce and transportation in the Texoma area.
-
B.
Sherman
Sherman is the bumbling yet kind-hearted scientist protagonist portrayed by Eddie Murphy in the comedy film "The Nutty Professor."
-
C.
Sherman
Sherman is a surname of English origin borne by numerous notable individuals across politics, military history, and the arts.
-
D.
The General
The General is a famous 19th-century American steam locomotive best known for its central role in the Civil War’s Great Locomotive Chase of 1862.
-
E.
The General
The General is a science fiction novella by Isaac Asimov, part of his Foundation series, focusing on the clash between psychohistory and the military genius of General Bel Riose.
- 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_69c6880687b08190805278b504d1c92c |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6b1955e448190adbfed7dc28f8c52 |
completed | March 27, 2026, 4:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6f7b97210819086e88624c476fa24 |
completed | March 27, 2026, 9:33 p.m. |
Created at: March 27, 2026, 2:05 p.m.