Triple
T10532400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weil, Gotshal & Manges LLP |
E248476
|
entity |
| Predicate | hasOfficeIn |
P1268
|
FINISHED |
| Object | Munich |
E21335
|
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: Munich | Statement: [Weil, Gotshal & Manges LLP, hasOfficeIn, Munich]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Munich Context triple: [Weil, Gotshal & Manges LLP, hasOfficeIn, Munich]
-
A.
Munich
"Munich" is a 2005 historical drama thriller film directed by Steven Spielberg that depicts the covert Israeli response to the 1972 Munich Olympics massacre.
-
B.
Munich
chosen
Munich is the capital and largest city of the German state of Bavaria, renowned for its rich cultural scene, historic architecture, and the annual Oktoberfest beer festival.
-
C.
Leverkusen
Leverkusen is a city in western Germany, known for its chemical industry and as the home of the football club Bayer 04 Leverkusen.
-
D.
Regensburg
Regensburg is a historic city in southeastern Germany known for its well-preserved medieval old town on the Danube River.
-
E.
Ingolstadt
Ingolstadt is a historic city in southern Germany known for its medieval architecture, university tradition, and role as a major hub of the automotive industry.
- 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_69d381c5c7448190bec34bee7ec72bac |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d50a17f23081909f3372e160e21670 |
completed | April 7, 2026, 1:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d96b3d6f6c81908d8247da9d9caab2 |
completed | April 10, 2026, 9:27 p.m. |
Created at: April 6, 2026, 12:30 p.m.