Triple
T11971470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battersea Park |
E284928
|
entity |
| Predicate | landscapedBy |
P19613
|
FINISHED |
| Object | John Gibson |
E876897
|
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: John Gibson | Statement: [Battersea Park, landscapedBy, John Gibson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Gibson Context triple: [Battersea Park, landscapedBy, John Gibson]
-
A.
John Gibson
John Gibson is an American professional ice hockey goaltender best known for his standout NHL career with the Anaheim Ducks and international play for Team USA.
-
B.
John Gibson
chosen
John Gibson was a 19th-century British architect known for designing prominent public buildings in a classical style.
-
C.
Michel Gibson
Michel Gibson is a local political figure who serves as the mayor of Kirkland, overseeing the city's municipal government and public affairs.
-
D.
Daniel Gibson
Daniel Gibson is a former American professional basketball player who played as a guard for the Cleveland Cavaliers in the NBA.
-
E.
Robert Gaskins
Robert Gaskins is a software entrepreneur best known as the co-creator of Microsoft PowerPoint and a key figure in the early development of presentation software.
- 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_69d6ab2eaeb881909f7914758f859413 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9037d32e88190b1509285dc907d29 |
completed | April 10, 2026, 2:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f63e99d88190b718217005464954 |
completed | May 2, 2026, 1:03 p.m. |
Created at: April 8, 2026, 9:46 p.m.