Triple
T10026713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Allen Covert |
E200747
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Allen |
E105455
|
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: Allen | Statement: [Allen Covert, givenName, Allen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Allen Context triple: [Allen Covert, givenName, Allen]
-
A.
Allen
chosen
Allen is a common English-language surname borne by numerous notable individuals across politics, sports, entertainment, and other fields.
-
B.
Allen
Allen is a city in Argentina’s Alto Valle region, known primarily for its fruit production and agriculture-based economy.
-
C.
Allen
Allen was the French-made leather football used as the official match ball of the 1938 FIFA World Cup.
-
D.
Allen
Allen is a suburban city in Collin County, Texas, known for its rapid growth, family-friendly communities, and location within the Dallas–Fort Worth metropolitan area.
-
E.
Anderson
Anderson is a city in South Carolina known as a regional hub in the Upstate area and part of the Greenville-Anderson-Mauldin metropolitan region.
- 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_69ca831c45f08190ac1505cc15076608 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cdcde3c1b88190924e6158f406b453 |
completed | April 2, 2026, 2:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d26ac2f14081908deaf3945491af78 |
completed | April 5, 2026, 1:59 p.m. |
Created at: March 30, 2026, 8:54 p.m.