Triple
T3887907
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ancienne Douane de Strasbourg |
E87987
|
entity |
| Predicate | hasFunctionInThePast |
P29363
|
FINISHED |
| Object | customs house |
—
|
LITERAL 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: customs house | Statement: [Ancienne Douane de Strasbourg, hasFunctionInThePast, customs house]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFunctionInThePast Context triple: [Ancienne Douane de Strasbourg, hasFunctionInThePast, customs house]
-
A.
hadFunction
Indicates that an entity previously served or fulfilled a particular role, purpose, or function.
-
B.
hasFunctionInComplex
Indicates that an entity performs a specific functional role within a larger molecular or structural complex.
-
C.
hasFormerUse
chosen
Indicates that something previously served a particular function or role that it no longer has.
-
D.
hasHistorySince
Indicates that an entity has maintained a particular state, condition, or relationship continuously starting from a specified point in time.
-
E.
hasHistoricalPrecursor
Indicates that one entity existed earlier and served as a predecessor, model, or influential forerunner to the other in a historical context.
- F. None of above.
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_69aed9466d548190939f5217a23ed4ac |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeecabe3548190a5cbf9d0af0bcfb6 |
completed | March 9, 2026, 3:52 p.m. |
| PD | Predicate disambiguation | batch_69aee759609c8190985e96ec6d96dedd |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:21 p.m.