Triple
T12175193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Module B (NIRCam) |
E290069
|
entity |
| Predicate | numberOfModulesInInstrument |
P19277
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Module B (NIRCam), numberOfModulesInInstrument, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfModulesInInstrument Context triple: [Module B (NIRCam), numberOfModulesInInstrument, 2]
-
A.
numberOfModules
chosen
Indicates the total count of modules associated with a given entity.
-
B.
numberOfInstruments
Indicates the quantitative count of instruments associated with or involved in a given entity or event.
-
C.
numberInInstrument
Indicates that an entity specifies the count or quantity of items contained within or associated with a particular instrument.
-
D.
includesInstruments
Indicates that one entity contains, involves, or makes use of one or more instruments as part of its composition or execution.
-
E.
numberOfInstrumentsMade
Indicates the quantity of instruments that have been produced or created by a given entity.
- 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_69d6ab4d6c00819095a9a7c35de83cfb |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91621ca6c81908365732f361aef13 |
completed | April 10, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69d9150e85348190b9b47cda4a17dcd0 |
completed | April 10, 2026, 3:19 p.m. |
Created at: April 8, 2026, 9:50 p.m.