Triple
T23021891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thales Defence & Security |
E573189
|
entity |
| Predicate | countryOfHeadquarters |
P62
|
FINISHED |
| Object | France |
—
|
NE NERFINISHED |
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: France | Statement: [Thales Defence & Security, countryOfHeadquarters, France]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: France Context triple: [Thales Defence & Security, countryOfHeadquarters, France]
-
A.
France
chosen
France is a major Western European nation known for its influential history, culture, and economy, and as a founding member of the European Union and the United Nations.
-
B.
La France
La France is a renowned sculpture by French artist Antoine Bourdelle that powerfully symbolizes the spirit and identity of France.
-
C.
Francia
Francia is the surname of American rower and two-time Olympic gold medalist Susan Francia.
-
D.
Pays Royannais
Pays Royannais is a coastal area in southwestern France centered around the town of Royan, known for its seaside resorts and Atlantic beaches.
-
E.
de France
"de France" is a dynastic surname historically used by members of the French royal family, particularly the legitimate children of reigning kings of France.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245b821008190b0e09cb02092aae1 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f183e9208081908fdbb8e21cacedde |
completed | April 29, 2026, 4:07 a.m. |
Created at: April 17, 2026, 3:52 p.m.