Triple
T1541314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Atacama Large Millimeter/submillimeter Array |
E32871
|
entity |
| Predicate | numberOfAntennas |
P30169
|
FINISHED |
| Object | 66 |
—
|
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: 66 | Statement: [Atacama Large Millimeter/submillimeter Array, numberOfAntennas, 66]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAntennas Context triple: [Atacama Large Millimeter/submillimeter Array, numberOfAntennas, 66]
-
A.
numberOfAntennasInitial
Indicates the initial count of antennas associated with an entity before any changes or updates occur.
-
B.
numberOfAntennasPlanned
Indicates the planned or intended count of antennas associated with an entity or installation.
-
C.
antennaType
Indicates the specific kind or category of antenna associated with an entity or connection.
-
D.
hasAntennae
Indicates that one entity possesses or is equipped with antennae as a physical feature.
-
E.
hasNumberOfElectrodes
Indicates the relationship that specifies how many electrodes are associated with a given entity.
- F. None of above. chosen
Provenance (4 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_69a885ed29088190a3c2d5a3d100c16e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa95c1a2948190a2b98469afec1a7d |
completed | March 6, 2026, 8:52 a.m. |
| PD | Predicate disambiguation | batch_69a907b2453c8190a41f6b88c8217d1e |
completed | March 5, 2026, 4:33 a.m. |
| PDg | Predicate description generation | batch_69a93df847cc8190b6d011af33b34b40 |
completed | March 5, 2026, 8:25 a.m. |
Created at: March 4, 2026, 7:26 p.m.