Triple
T1956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belmont, Massachusetts |
E36
|
entity |
| Predicate | population |
P328
|
FINISHED |
| Object | approximately 27,000 |
—
|
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: approximately 27,000 | Statement: [Belmont, Massachusetts, population, approximately 27,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: population Context triple: [Belmont, Massachusetts, population, approximately 27,000]
-
A.
demographicsNote
Indicates that there is an associated note or commentary describing demographic-related information about an entity.
-
B.
populationCensusYear
Indicates the specific year in which an official population census was conducted or recorded for an entity.
-
C.
demographicsCharacteristic
Indicates that one entity serves as a demographic attribute or characteristic (such as age, gender, ethnicity, etc.) that describes or classifies another entity.
-
D.
residence
Indicates that one entity lives at, is based in, or habitually occupies the location represented by the other entity.
-
E.
partOfMetropolitanArea
Indicates that one place is included within and belongs to the larger metropolitan area of another place.
- 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_69a22cde80848190b62c5f556b4d62ba |
completed | Feb. 27, 2026, 11:46 p.m. |
| NER | Named-entity recognition | batch_69a2346846608190b6b40d31f1dbd685 |
completed | Feb. 28, 2026, 12:18 a.m. |
| PD | Predicate disambiguation | batch_69a233c396ec8190986608d07fb251d4 |
completed | Feb. 28, 2026, 12:16 a.m. |
| PDg | Predicate description generation | batch_69a2346794cc8190afce97b703903389 |
completed | Feb. 28, 2026, 12:18 a.m. |
Created at: Feb. 27, 2026, 11:48 p.m.