Triple
T606367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Portrait of Antoine-Laurent Lavoisier and his Wife |
E12002
|
entity |
| Predicate | inventoryNumber |
P3732
|
FINISHED |
| Object | 1977.10 |
—
|
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: 1977.10 | Statement: [Portrait of Antoine-Laurent Lavoisier and his Wife, inventoryNumber, 1977.10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inventoryNumber Context triple: [Portrait of Antoine-Laurent Lavoisier and his Wife, inventoryNumber, 1977.10]
-
A.
titleNumber
Indicates the numerical designation or sequence number assigned to a title within an ordered set of titles.
-
B.
identifierFor
chosen
Indicates that one entity serves as a unique identifying label or code for another entity.
-
C.
modelNumber
Indicates that one entity is the specific model identifier or code assigned to another entity (such as a product or device).
-
D.
pennantNumber
Indicates the identifying pennant number assigned to a ship or naval vessel.
-
E.
number
Indicates that one entity is associated with a specific numerical value or count in relation to another entity or 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_69a493309df48190a327f748e88049a6 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49df34abc8190a578c8c2ab3d28e4 |
completed | March 1, 2026, 8:13 p.m. |
| PD | Predicate disambiguation | batch_69a49cf8fc1c81908a9c7df552aa1a59 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:35 p.m.